{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1c2cca7e"
      },
      "source": [
        "# Distilling BERT models"
      ],
      "id": "1c2cca7e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zbofv-VzcCt6"
      },
      "outputs": [],
      "source": [
        "!pip install datasets transformers[torch] accelerate"
      ],
      "id": "Zbofv-VzcCt6"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HEhdQhSTGxqU"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset"
      ],
      "id": "HEhdQhSTGxqU"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284,
          "referenced_widgets": [
            "ab4aa01f2d324627848479353091dc1a",
            "44a11aab58f14a39a91327a87b0b054e",
            "96381e2806f7459784df35c93dcf54e1",
            "f48b5dc6b9b44d788ede74913e450e33",
            "c2c676f40132414b8bfd9e2c0ae1fba4",
            "231af7e8934e41289910429856e8f56d",
            "3854b57c21604e15bf88ab4a994b330c",
            "6237876f4bbb4d7ba682382f91abe313",
            "652c2919783a402482b4750c5aa3f09a",
            "2ac833eeb215477e8e7b2130cd60a39c",
            "fbe0620a5eb64fdbba32ab871a85fb85",
            "63f2987373f34479a8d48c296a994061",
            "0088e263c9d54cc484fe88248e28b82d",
            "ef6588037acd4f69a37224e29b5b1d8f",
            "17f172236c6c429bbc9aa366879c041a",
            "af6dd55a33ec413ea58854acd5725dc2",
            "29e2e2e564244af3a09f5c2e190a6612",
            "199fb0268d514892b71e72118ae1b84c",
            "ae9ad1fbca414ee199ee5cb0528c5726",
            "9ae3f0e77b88433699982d75b7c27283",
            "eab1c44c7c21493d9febc71fa380f5bd",
            "0f243ba9b9ba4c9e86a37257ee64309e",
            "0438144f3cb245bba838963bef46d5d9",
            "2ba5cbc3e0194d1392efe3bc9375f327",
            "db49f1d979e245d8814d9713c3a17a2b",
            "12720636e0764b01b1b87e2dda06b6cf",
            "1347518918364b8082cc30e6ae2625d0",
            "a970a799e6fe4be5a1df7fe64e9ad351",
            "ca7dcc3539514f79aa27c350e1ef2fab",
            "7571123c739244a7b3b7cf66869e3aeb",
            "2cbb854418e74dbaae41479f3d2f43c6",
            "c288b9079a6a47b69d2b95aca4c0cf68",
            "9ad3df398ce34b498e1cb0146f66621a",
            "fc22c935464b4386b31d8c821a548e4e",
            "23ebb4d07b544c5bbfe3780be9fd2bd9",
            "5dbf80ee28a54f61a1c94de7ae4373ac",
            "fdb6a7cea93a40ab8e2d96540f455470",
            "0585b73e604644e0a9a91e2dffe3501b",
            "d903c882125a46c1a3c9646d4067f805",
            "a57b66098dad4dce83262f642d9bab80",
            "e164ae793b204d63a379fe0e84d50ee0",
            "6b2f0c733c184f6fa88ba94c37666a90",
            "9dad78bcfb874cabae544cf98836e315",
            "391e63838ff04751ab97760c4cceb7d5"
          ]
        },
        "id": "qT3xQnLzGtFO",
        "outputId": "30d86433-bb35-4fde-c8e2-f53491bf82cc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/9.40k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab4aa01f2d324627848479353091dc1a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/24.8M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63f2987373f34479a8d48c296a994061"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/211225 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0438144f3cb245bba838963bef46d5d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/211225 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc22c935464b4386b31d8c821a548e4e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'id', 'author', 'subreddit', 'link_id', 'parent_id', 'created_utc', 'rater_id', 'example_very_unclear', 'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral', 'labels'],\n",
              "    num_rows: 211225\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "dataset = load_dataset(\"go_emotions\", \"raw\")['train']\n",
        "\n",
        "label_names = dataset.column_names[10:]\n",
        "\n",
        "dataset = dataset.map(\n",
        "    lambda x: {'labels': [label for label in label_names if x[label]]}\n",
        ")\n",
        "\n",
        "dataset"
      ],
      "id": "qT3xQnLzGtFO"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbZ4GtK6nF72",
        "outputId": "1012e1e7-e7ba-4b7d-85da-adb082e7d80b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'id', 'author', 'subreddit', 'link_id', 'parent_id', 'created_utc', 'rater_id', 'example_very_unclear', 'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral', 'labels'],\n",
              "        num_rows: 168980\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'id', 'author', 'subreddit', 'link_id', 'parent_id', 'created_utc', 'rater_id', 'example_very_unclear', 'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral', 'labels'],\n",
              "        num_rows: 42245\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset = dataset.train_test_split(test_size=0.2, shuffle=True)\n",
        "dataset"
      ],
      "id": "LbZ4GtK6nF72"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8c26e4b",
        "outputId": "b3c14905-f581-4f58-b73e-afb7e2964266"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['approval', 'optimism', 'annoyance']"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from functools import reduce\n",
        "\n",
        "all_labels = list(reduce(lambda y, z: y + z, dataset['train'].select(range(10_000))['labels']))\n",
        "all_labels[:3]"
      ],
      "id": "d8c26e4b"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 664
        },
        "id": "94e41b0c",
        "outputId": "76dea670-12bd-44db-e3dc-b6fd3fe1fac4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-8-c9bd737b9909>:16: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=genre_counts.index, y=genre_counts.values, palette=colors)\n",
            "<ipython-input-8-c9bd737b9909>:16: UserWarning: \n",
            "The palette list has fewer values (10) than needed (27) and will cycle, which may produce an uninterpretable plot.\n",
            "  sns.barplot(x=genre_counts.index, y=genre_counts.values, palette=colors)\n"
          ]
        },
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "# Set style and context\n",
        "sns.set_style('whitegrid')\n",
        "sns.set_context('notebook')\n",
        "\n",
        "# Prepare the data\n",
        "genre_counts = pd.Series(all_labels).value_counts()\n",
        "\n",
        "# Create the plot\n",
        "plt.figure(figsize=(16, 6))\n",
        "\n",
        "colors = sns.color_palette('pastel')[0:len(genre_counts)]\n",
        "sns.barplot(x=genre_counts.index, y=genre_counts.values, palette=colors)\n",
        "\n",
        "plt.title('Distribution of Labels', fontsize=20)\n",
        "plt.xlabel('Genre', fontsize=14)\n",
        "plt.ylabel('Count', fontsize=14)\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "# Show and save the plot\n",
        "plt.show()"
      ],
      "id": "94e41b0c"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9d231d56",
        "outputId": "af0de4ef-b791-4826-ab83-a56acb4e03e6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "27 ['amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'neutral', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise']\n"
          ]
        }
      ],
      "source": [
        "unique_labels = sorted(list(set(all_labels)))\n",
        "\n",
        "print(len(unique_labels), unique_labels)"
      ],
      "id": "9d231d56"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fa97c096"
      },
      "outputs": [],
      "source": [
        "id2label = {idx:label for idx, label in enumerate(unique_labels)}\n",
        "label2id = {label:idx for idx, label in enumerate(unique_labels)}\n"
      ],
      "id": "fa97c096"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "09769857b0074f4aa236bca7488ee96b",
            "2e2f139d89ea4f95b8d9c8c1b74880ee",
            "dbe5c521509442769030c8818e489d56",
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          ]
        },
        "id": "0ca911f9",
        "outputId": "eef27220-ab60-413f-dab5-3c42ee45fdb2",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "09769857b0074f4aa236bca7488ee96b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e63342a210eb4f25a638b54a2cd1aa5f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/762 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a587934f04447879fe7e90f209d312c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8c8582979d76432abaa17f64cf45ce36",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "TEACHER_MODEL = 'bert-large-cased'\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(TEACHER_MODEL)\n",
        "\n",
        "def preprocess_data(examples, text_col='text', label_col='labels'):\n",
        "    one_hot_encoded_matrix = []\n",
        "    text = examples[text_col]\n",
        "    labels = examples[label_col]\n",
        "    for label in labels:\n",
        "        one_hot_encoded_matrix.append([1 if l in label else 0 for l in unique_labels])\n",
        "\n",
        "    # Convert the one_hot_encoded_matrix to a LongTensor\n",
        "    one_hot_encoded_matrix = [torch.tensor(o, dtype=torch.float32) for o in one_hot_encoded_matrix]\n",
        "\n",
        "    # Encode the text\n",
        "    encoding = tokenizer(text, truncation=True, max_length=256)\n",
        "\n",
        "    # Add labels\n",
        "    encoding[\"labels\"] = one_hot_encoded_matrix\n",
        "\n",
        "    return encoding\n"
      ],
      "id": "0ca911f9"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "8dc87cb14e2d4b3f9007640d0ab227a4",
            "d893fa0d3cf04161a125d8106f86bd8d",
            "da400c3b851f4b74ab1937c55e621e22",
            "40dbefb70ccb42a6ab4f208d9e47e550",
            "e24bc346e90747118d3dfedb0286f4e1",
            "8fe3a175096443a2af3fc2390bb87b70",
            "a8d91d70246d47d49f25480086dfef49",
            "8722573a488f43a5b36da8ae5faa3274",
            "ed7ed2947edb41d587ab5bac901f25da",
            "5086418cc6fb47dc9e0497cf0b4268b0",
            "6472681fbb25423d92d41a25039c0862",
            "27891a720f5e4f92940f1dd190ba1667",
            "685097dfbf384180b828de7ef5b05487",
            "a2eb458622eb441d9cbb303b230a7daa",
            "f4aecf2f850447e8a8f7d4ba83b403f9",
            "a55a8cb72cfa4581a63a996cf9058cae",
            "51c79f192dec474f85e0e3d746f77454",
            "efc0211c658b4c029f17fe6435f18315",
            "3bcf1f296bec4852b81d066736b2c773",
            "88c0d6e5393a414780e1f903df78bbaf",
            "baf80a6770fe4f8ba2d5c3a5cddd9cba",
            "3c6f6326ab5543bc8fe3d90eb8fa6111"
          ]
        },
        "id": "90ef5196",
        "outputId": "9be01e3f-8198-4093-b4f0-240bd27db16f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8dc87cb14e2d4b3f9007640d0ab227a4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Filter:   0%|          | 0/168980 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "27891a720f5e4f92940f1dd190ba1667",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Filter:   0%|          | 0/42245 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# MAX_TEXT_LENGTH = 1_000\n",
        "# if MAX_TEXT_LENGTH:\n",
        "#     dataset = dataset.filter(lambda x: len(x['text']) < MAX_TEXT_LENGTH)"
      ],
      "id": "90ef5196"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402,
          "referenced_widgets": [
            "4515f09489ae413d841b810c5b9c0810",
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            "223c813deeff4655867c2ed482a62c20",
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            "b89d95df6a3b4c568e9e084eab287f59",
            "4385c1a055b04a48a1b4010e580b3070",
            "eeb0fc6b4f7c4fb2b65c5920cdb8c2ee",
            "54afb8ecc02747cb969c5be2297c67c3",
            "029cc912794047548ff738cf4bdf93af",
            "054ed2b47b444c898c87d4f6cffa9ba5",
            "a5f85c7b869f4462916be6846310078d",
            "a15523295c774cd6849ec65e7186535a",
            "a895443e0b1e46d5bcc7fed2ce1e1f02",
            "d8868ce9626b4b639f4f2e208f677afc",
            "153f2f3f84e74edaa8c3941bd33a40ae",
            "871c0d65029f41d0ba1c57cfbc1b844e",
            "9d214deae6a945cda7cf7bb0a5546331",
            "a2294ce29e524f20a9c4a835e7041224",
            "2b63781151244f95ba7ba470d6b97410",
            "d88e5da6bb6b489c869ba1ce3eb8a073",
            "8591ea9a1b3d48b7ad5d7ceb0127948e"
          ]
        },
        "id": "RnhC0gexgr_4",
        "outputId": "6d907be2-e231-4c2f-940e-b960a014f808"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4515f09489ae413d841b810c5b9c0810",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/168980 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a5f85c7b869f4462916be6846310078d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/42245 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'id', 'author', 'subreddit', 'link_id', 'parent_id', 'created_utc', 'rater_id', 'example_very_unclear', 'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral', 'labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 168980\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'id', 'author', 'subreddit', 'link_id', 'parent_id', 'created_utc', 'rater_id', 'example_very_unclear', 'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral', 'labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 42245\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "encoded_dataset = dataset.map(preprocess_data, batched=True, batch_size=128)\n",
        "\n",
        "encoded_dataset"
      ],
      "id": "RnhC0gexgr_4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2716947f",
        "outputId": "6af2c316-b10d-46a3-d5f7-2bb88529ba50"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(encoded_dataset['train'][0]['input_ids'])"
      ],
      "id": "2716947f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9beb057f"
      },
      "outputs": [],
      "source": [
        "# Import the necessary libraries\n",
        "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score\n",
        "from transformers import EvalPrediction\n",
        "import torch\n",
        "import numpy as np\n",
        "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score, jaccard_score, precision_score, recall_score\n",
        "\n",
        "# Define a function to compute several multi-label metrics\n",
        "def multi_label_metrics(predictions, labels, threshold=0.5):\n",
        "    # Initialize the sigmoid function which we'll use to transform our raw prediction values\n",
        "    sigmoid = torch.nn.Sigmoid()\n",
        "\n",
        "    # Apply sigmoid function to our predictions\n",
        "    probs = sigmoid(torch.Tensor(predictions))\n",
        "\n",
        "    # Create a binary prediction array based on our threshold\n",
        "    y_pred = np.zeros(probs.shape)\n",
        "    y_pred[np.where(probs >= threshold)] = 1\n",
        "\n",
        "    # Use actual labels as y_true\n",
        "    y_true = labels\n",
        "\n",
        "    # Compute F1 score, ROC AUC score, Accuracy, and Jaccard score\n",
        "    f1_micro_average = f1_score(y_true=y_true, y_pred=y_pred, average='micro')\n",
        "    roc_auc = roc_auc_score(y_true, y_pred, average='micro')\n",
        "    accuracy = accuracy_score(y_true, y_pred)\n",
        "    jaccard = jaccard_score(y_true, y_pred, average='micro')\n",
        "    precision = precision_score(y_true, y_pred, average='micro')\n",
        "    recall = recall_score(y_true, y_pred, average='micro')\n",
        "\n",
        "    # Package the scores into a dictionary and return it\n",
        "    metrics = {\n",
        "        'f1': f1_micro_average,\n",
        "               'roc_auc': roc_auc,\n",
        "               'accuracy': accuracy,\n",
        "               'jaccard': jaccard,\n",
        "               'precision': precision,\n",
        "               'recall': recall\n",
        "        }\n",
        "    return metrics\n",
        "\n",
        "# Define a function to compute metrics for predictions\n",
        "def compute_metrics(p: EvalPrediction):\n",
        "    # Extract the prediction values from the EvalPrediction object\n",
        "    preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions\n",
        "\n",
        "    # Compute the multi-label metrics for the predictions and actual labels\n",
        "    result = multi_label_metrics(predictions=preds, labels=p.label_ids)\n",
        "\n",
        "    # Return the results\n",
        "    return result\n"
      ],
      "id": "9beb057f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "082ebc33"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer, TrainingArguments, DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "id": "082ebc33"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "249f8159"
      },
      "source": [
        "# Train with normal BERT"
      ],
      "id": "249f8159"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "a68c5d55d0224f11b540b526bad9e5f1",
            "c9fbbb20eab94eba80ac950b436f3452",
            "8218c67e8b9c4f00a934b0ad58f6966e",
            "97725a64dc1e4a56af81fc437e6df782",
            "012254a119954b12bde4ea86e6ecf57e",
            "2a011a7eea94444d9a96f7faee06e664",
            "cb41e4e318e34dbb991bf1a8db9cafb1",
            "7685fd5a51bf4e21996c7be111e1b4c5",
            "e510cbf070064f8ab83cbc027252c97d",
            "b8dcd5b522a540089fd160a1ffbf3a28",
            "53c006c8006e4c838a978f771e6f491a"
          ]
        },
        "id": "6dd021da",
        "outputId": "b3b74594-f429-41c7-851e-ebf236fa70d4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a68c5d55d0224f11b540b526bad9e5f1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.34G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-large-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "teacher_model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    TEACHER_MODEL,\n",
        "    problem_type=\"multi_label_classification\",\n",
        "    num_labels=len(unique_labels),\n",
        "    id2label=id2label,\n",
        "    label2id=label2id\n",
        ")"
      ],
      "id": "6dd021da"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K4IgWWJKsqzz"
      },
      "outputs": [],
      "source": [
        "EPOCHS = 3"
      ],
      "id": "K4IgWWJKsqzz"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "41d21774",
        "outputId": "386e1c64-e697-4538-b913-e53ec0016a76",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='661' max='661' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [661/661 00:33]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "{'eval_loss': 0.6257067322731018,\n",
              " 'eval_f1': 0.05821833534712295,\n",
              " 'eval_roc_auc': 0.4739992108712018,\n",
              " 'eval_accuracy': 0.0,\n",
              " 'eval_jaccard': 0.029981916302382207,\n",
              " 'eval_precision': 0.03340313471666984,\n",
              " 'eval_recall': 0.22644269029746414,\n",
              " 'eval_runtime': 35.8614,\n",
              " 'eval_samples_per_second': 1178.008,\n",
              " 'eval_steps_per_second': 18.432}"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "args = TrainingArguments(\n",
        "    output_dir=\"teacher-bert\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    per_device_train_batch_size=4,\n",
        "    gradient_accumulation_steps=16,  # effective batch size of 64\n",
        "    per_device_eval_batch_size=64,\n",
        "    num_train_epochs=EPOCHS,\n",
        "    logging_steps=10,\n",
        "    load_best_model_at_end=True,\n",
        "    fp16=True,\n",
        "    learning_rate=2e-5,\n",
        "    warmup_ratio=0.1,\n",
        "    lr_scheduler_type='cosine'\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    teacher_model,\n",
        "    args,\n",
        "    train_dataset=encoded_dataset[\"train\"],\n",
        "    eval_dataset=encoded_dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics\n",
        ")\n",
        "\n",
        "trainer.evaluate()"
      ],
      "id": "41d21774"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "0757706c",
        "scrolled": true,
        "outputId": "d994891b-0f28-45e7-afaf-c7ec980b112c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='7920' max='7920' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [7920/7920 3:15:10, Epoch 2/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "      <th>Roc Auc</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Jaccard</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.112100</td>\n",
              "      <td>0.111386</td>\n",
              "      <td>0.308527</td>\n",
              "      <td>0.599834</td>\n",
              "      <td>0.232359</td>\n",
              "      <td>0.182401</td>\n",
              "      <td>0.624107</td>\n",
              "      <td>0.204913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.102800</td>\n",
              "      <td>0.108915</td>\n",
              "      <td>0.334620</td>\n",
              "      <td>0.611676</td>\n",
              "      <td>0.247580</td>\n",
              "      <td>0.200927</td>\n",
              "      <td>0.618382</td>\n",
              "      <td>0.229368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.099300</td>\n",
              "      <td>0.109416</td>\n",
              "      <td>0.360622</td>\n",
              "      <td>0.626549</td>\n",
              "      <td>0.272056</td>\n",
              "      <td>0.219975</td>\n",
              "      <td>0.583097</td>\n",
              "      <td>0.261029</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1322' max='661' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [661/661 1:05:08]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=7920, training_loss=0.11962228253333256, metrics={'train_runtime': 11712.0905, 'train_samples_per_second': 43.283, 'train_steps_per_second': 0.676, 'total_flos': 2.778198275811576e+16, 'train_loss': 0.11962228253333256, 'epoch': 3.0})"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ],
      "id": "0757706c"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "oSYUrcSIgQ-A"
      },
      "outputs": [],
      "source": [
        "# trainer.evaluate(eval_dataset=encoded_dataset[\"valid\"])"
      ],
      "id": "oSYUrcSIgQ-A"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "a0b28568"
      },
      "outputs": [],
      "source": [
        "trainer.save_model()"
      ],
      "id": "a0b28568"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ef16181a"
      },
      "source": [
        "# Train with Distil BERT (Task-Agnostic Distilation)"
      ],
      "id": "ef16181a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "referenced_widgets": [
            "42691b531a0541ef8c554c02efbcbc41",
            "6455d3afd6614c1990af1794968d6fbe"
          ]
        },
        "id": "a78bf3e8",
        "outputId": "3b925c99-9ab0-4a2d-9f0c-596da6056528"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "42691b531a0541ef8c554c02efbcbc41",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/465 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6455d3afd6614c1990af1794968d6fbe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/263M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "ta_distil_model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    'distilbert-base-cased',\n",
        "    problem_type=\"multi_label_classification\",\n",
        "    num_labels=len(unique_labels),\n",
        "    id2label=id2label,\n",
        "    label2id=label2id\n",
        ")"
      ],
      "id": "a78bf3e8"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "f2d76792",
        "scrolled": true,
        "outputId": "7c6851c1-ddac-4773-a52a-56e48a4aa808"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='661' max='661' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [661/661 00:10]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "{'eval_loss': 0.6984817981719971,\n",
              " 'eval_f1': 0.0466455019326153,\n",
              " 'eval_roc_auc': 0.40516215216686385,\n",
              " 'eval_accuracy': 0.0,\n",
              " 'eval_jaccard': 0.023879691053910367,\n",
              " 'eval_precision': 0.02530415048002602,\n",
              " 'eval_recall': 0.2978512894414213,\n",
              " 'eval_runtime': 11.634,\n",
              " 'eval_samples_per_second': 3631.172,\n",
              " 'eval_steps_per_second': 56.816}"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "args = TrainingArguments(\n",
        "    f\"task-agnostic-distilbert\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    per_device_train_batch_size=16,\n",
        "    gradient_accumulation_steps=4,\n",
        "    per_device_eval_batch_size=64,\n",
        "    num_train_epochs=EPOCHS,\n",
        "    logging_steps=10,\n",
        "    load_best_model_at_end=True,\n",
        "    fp16=True,\n",
        "    learning_rate=2e-5,\n",
        "    warmup_ratio=0.1,\n",
        "    lr_scheduler_type='cosine'\n",
        ")\n",
        "\n",
        "ta_trainer = Trainer(\n",
        "    ta_distil_model,\n",
        "    args,\n",
        "    train_dataset=encoded_dataset[\"train\"],\n",
        "    eval_dataset=encoded_dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics\n",
        ")\n",
        "\n",
        "ta_trainer.evaluate()"
      ],
      "id": "f2d76792"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "379a486f",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "97b4bf73-d044-4e92-a9eb-1884f58704f9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='473' max='7920' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 473/7920 00:54 < 14:18, 8.68 it/s, Epoch 0.18/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='7920' max='7920' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [7920/7920 16:25, Epoch 2/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "      <th>Roc Auc</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Jaccard</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.116400</td>\n",
              "      <td>0.113137</td>\n",
              "      <td>0.271160</td>\n",
              "      <td>0.583455</td>\n",
              "      <td>0.206226</td>\n",
              "      <td>0.156845</td>\n",
              "      <td>0.659737</td>\n",
              "      <td>0.170649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.108500</td>\n",
              "      <td>0.109893</td>\n",
              "      <td>0.330371</td>\n",
              "      <td>0.609469</td>\n",
              "      <td>0.246467</td>\n",
              "      <td>0.197871</td>\n",
              "      <td>0.623664</td>\n",
              "      <td>0.224700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1322' max='661' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [661/661 05:32]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=7920, training_loss=0.12881591982311671, metrics={'train_runtime': 985.9467, 'train_samples_per_second': 514.166, 'train_steps_per_second': 8.033, 'total_flos': 4862772610851168.0, 'train_loss': 0.12881591982311671, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "ta_trainer.train()"
      ],
      "id": "379a486f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "348f1b00"
      },
      "outputs": [],
      "source": [
        "# ta_trainer.evaluate(eval_dataset=encoded_dataset[\"valid\"])"
      ],
      "id": "348f1b00"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5302ebf0"
      },
      "outputs": [],
      "source": [],
      "id": "5302ebf0"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ff565044",
        "scrolled": true
      },
      "outputs": [],
      "source": [],
      "id": "ff565044"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c38b4619"
      },
      "source": [
        "# (Task-Specific Distilation)"
      ],
      "id": "c38b4619"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2eea2100"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "# Custom TrainingArguments class to add distillation specific parameters\n",
        "class DistillationTrainingArguments(TrainingArguments):\n",
        "    def __init__(self, *args, alpha=0.5, temperature=2.0, **kwargs):\n",
        "        super().__init__(*args, **kwargs)\n",
        "\n",
        "        # Alpha is the weight for the original student loss\n",
        "        # A higher value means more focus on the student's original task\n",
        "        self.alpha = alpha\n",
        "\n",
        "        # Temperature parameter to soften probability distributions before calculating distillation loss\n",
        "        # Higher value makes the distribution more uniform, carrying more information from the teacher model's outputs\n",
        "        self.temperature = temperature\n",
        "\n",
        "# Custom Trainer class to implement knowledge distillation\n",
        "class DistillationTrainer(Trainer):\n",
        "    def __init__(self, *args, teacher_model=None, **kwargs):\n",
        "        super().__init__(*args, **kwargs)\n",
        "\n",
        "        # The teacher model, a pre-trained model that the student model will learn from\n",
        "        self.teacher = teacher_model\n",
        "\n",
        "        # Move the teacher model to the same device as the student model\n",
        "        # This is necessary for the computations in the forward pass\n",
        "        self._move_model_to_device(self.teacher, self.model.device)\n",
        "\n",
        "        # Set teacher model to eval mode because we only want to use it for inference, not for training\n",
        "        self.teacher.eval()\n",
        "\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        # Compute the output of the student model on the inputs\n",
        "        outputs_student = model(**inputs)\n",
        "        # Original loss of the student model (e.g., cross entropy for classification)\n",
        "        student_loss = outputs_student.loss\n",
        "\n",
        "        # Compute the output of the teacher model on the inputs\n",
        "        # We don't need gradients for the teacher model, so we use torch.no_grad to avoid unnecessary computation\n",
        "        with torch.no_grad():\n",
        "            outputs_teacher = self.teacher(**inputs)\n",
        "\n",
        "        # Check that the sizes of the student and teacher outputs match\n",
        "        assert outputs_student.logits.size() == outputs_teacher.logits.size()\n",
        "\n",
        "        # Kullback-Leibler divergence loss function, comparing the softened output distributions of the student and teacher models\n",
        "        loss_function = nn.KLDivLoss(reduction=\"batchmean\")\n",
        "\n",
        "        # Calculate the distillation loss between the student and teacher outputs\n",
        "        # We apply log_softmax to the student's outputs and softmax to the teacher's outputs before calculating the loss\n",
        "        # This is due to the expectation of log probabilities for the input and probabilities for the target in nn.KLDivLoss\n",
        "\n",
        "        # Note multiplying by temperature^2 is not to punish the loss like we did during reward modeling for SAWYER..\n",
        "        # It is done because we divided both student and teacher logits by temperature so this operation puts the loss\n",
        "        # back on the same scale as student_loss, which we are about to combine using a weighted mean\n",
        "        loss_logits = (loss_function(\n",
        "            F.log_softmax(outputs_student.logits / self.args.temperature, dim=-1),\n",
        "            F.softmax(outputs_teacher.logits / self.args.temperature, dim=-1)) * (self.args.temperature ** 2))\n",
        "\n",
        "        # The total loss is a weighted combination of the student's original loss and the distillation loss\n",
        "        loss = self.args.alpha * student_loss + (1. - self.args.alpha) * loss_logits\n",
        "\n",
        "        # Depending on the return_outputs parameter, return either the loss alone or the loss and the student's outputs\n",
        "        return (loss, outputs_student) if return_outputs else loss\n"
      ],
      "id": "2eea2100"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0e7a7950",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(TEACHER_MODEL)\n",
        "\n",
        "teacher_model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    f\"teacher-bert\", problem_type=\"multi_label_classification\",\n",
        ").eval()\n"
      ],
      "id": "0e7a7950"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "31536062",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63e229dd-f33b-478f-86b7-500d876edc54"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# define student model\n",
        "ts_distil_model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    'distilbert-base-cased',\n",
        "    num_labels=len(unique_labels),\n",
        "    id2label=id2label,\n",
        "    label2id=label2id,\n",
        ")"
      ],
      "id": "31536062"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8a5f0d0e"
      },
      "outputs": [],
      "source": [
        "# define training args\n",
        "training_args = DistillationTrainingArguments(\n",
        "    output_dir='task-specific-distilbert',\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    num_train_epochs=EPOCHS,\n",
        "    logging_steps=10,\n",
        "    per_device_train_batch_size=16,\n",
        "    gradient_accumulation_steps=4,\n",
        "    per_device_eval_batch_size=64,\n",
        "    load_best_model_at_end=True,\n",
        "    alpha=0.3,\n",
        "    temperature=2.0,\n",
        "    fp16=True,\n",
        "    learning_rate=2e-5,\n",
        "    warmup_ratio=0.1,\n",
        "    lr_scheduler_type='cosine'\n",
        "    )\n"
      ],
      "id": "8a5f0d0e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4fb3606a"
      },
      "outputs": [],
      "source": [
        "ts_trainer = DistillationTrainer(\n",
        "    ts_distil_model,\n",
        "    training_args,\n",
        "    teacher_model=teacher_model,\n",
        "    train_dataset=encoded_dataset[\"train\"],\n",
        "    eval_dataset=encoded_dataset[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n"
      ],
      "id": "4fb3606a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9f398e8a",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "outputId": "6f91a604-05c9-408f-b706-f0faa18046bd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='661' max='661' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [661/661 02:05]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 1.7064504623413086,\n",
              " 'eval_f1': 0.08556322703563403,\n",
              " 'eval_roc_auc': 0.5384727582076385,\n",
              " 'eval_accuracy': 0.0,\n",
              " 'eval_jaccard': 0.044693681318681316,\n",
              " 'eval_precision': 0.04556721994723014,\n",
              " 'eval_recall': 0.6998257802249801,\n",
              " 'eval_runtime': 126.7988,\n",
              " 'eval_samples_per_second': 333.166,\n",
              " 'eval_steps_per_second': 5.213}"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "ts_trainer.evaluate()"
      ],
      "id": "9f398e8a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "39d08276",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "4c4e1ce2-06c4-4b39-8c65-a8d1195c5fea"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='7920' max='7920' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [7920/7920 37:51, Epoch 2/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "      <th>Roc Auc</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Jaccard</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.137600</td>\n",
              "      <td>0.103033</td>\n",
              "      <td>0.397025</td>\n",
              "      <td>0.654413</td>\n",
              "      <td>0.290472</td>\n",
              "      <td>0.247680</td>\n",
              "      <td>0.518957</td>\n",
              "      <td>0.321489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.085400</td>\n",
              "      <td>0.069835</td>\n",
              "      <td>0.376422</td>\n",
              "      <td>0.637185</td>\n",
              "      <td>0.285762</td>\n",
              "      <td>0.231847</td>\n",
              "      <td>0.558352</td>\n",
              "      <td>0.283914</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1322' max='661' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [661/661 14:37]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=7920, training_loss=0.19167870295168174, metrics={'train_runtime': 2271.8125, 'train_samples_per_second': 223.143, 'train_steps_per_second': 3.486, 'total_flos': 4862772610851168.0, 'train_loss': 0.19167870295168174, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "ts_trainer.train()"
      ],
      "id": "39d08276"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2f17b844",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "# ts_trainer.evaluate(eval_dataset=encoded_dataset[\"valid\"])"
      ],
      "id": "2f17b844"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "64f21496"
      },
      "outputs": [],
      "source": [
        "ts_trainer.save_model()"
      ],
      "id": "64f21496"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1b15cceb",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "b80752dd-839c-4805-b814-94de28126bed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_metric(metric):\n",
        "\n",
        "    # Jaccard scores for each model (replace with your values)\n",
        "    teacher_metrics = [_[metric] for _ in trainer.state.log_history if metric in _][:-1]\n",
        "    agnostic_metrics = [_[metric] for _ in ta_trainer.state.log_history if metric in _][:-1]\n",
        "    specific_metrics = [_[metric] for _ in ts_trainer.state.log_history if metric in _][:-1]\n",
        "\n",
        "    range_epochs = range(1, len(teacher_metrics) + 1)\n",
        "\n",
        "    plt.figure(figsize=(8, 4))\n",
        "    plt.plot(range_epochs, teacher_metrics, marker='o', markersize=4, linestyle='-', label='Teacher Model')\n",
        "    plt.plot(range_epochs, agnostic_metrics, marker='s', markersize=4, linestyle='--', label='Task Agnostic Model')\n",
        "    plt.plot(range_epochs, specific_metrics, marker='^', markersize=4, linestyle='-.', label='Task Specific Model')\n",
        "\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel(metric.title())\n",
        "    plt.title(f'{metric.title()} over Epochs for Different Models')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "\n",
        "plot_metric('eval_jaccard')"
      ],
      "id": "1b15cceb"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "028bc464",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "55bd97b5-f333-45f5-c181-81ca9d35ae2c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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PtnvdokULIiIiiImJAdLH2n7//fc3/Sg7J7Zt24aDg4PdUASwtqFhGGzbts0u/c4776ROnTp5Pi5gG88bGxubL+VFRESwa9cuevXqZfvU6urVq4SHh9OuXTtOnDiRaZjLfffdZzckYceOHURFRREcHGz3HjSbzTRu3DjTexBgyJAhdq+bN2/OmTNn8uWc2rZti7e3N9988w2GYbBhwwaCg4OzzJvT38cdO3aQnJzM0KFD7d6Dw4cPz1Tmpk2baN68OV5eXnbXo02bNqSmpvK///0v27p7eXmxf//+LIcWye1PQylEMmjUqNENH75zdHSke/fuhIaGkpSUhLOzM9999x3Jycl2gTHA119/zfLlyzl+/Ljdx3xZzXqRW+vWrcPd3Z1q1apx8uRJwPokeJUqVVi/fr3t491Tp05hNptvOI3bqVOnAKhbt26e65VRVudpsVhYtWoVH3/8MWfOnLGNZwTshkOcOnWKmjVr4uh44z9Rffv25ZVXXuHs2bNUqVKFTZs2kZycnOU0URmdPXsWs9mcabaRcuXK4eXlxdmzZ21pvXv3ZsuWLezdu5dmzZpx6tQpDh48aPdR/YkTJzAMg+7du2d5vOvPo0KFCpke1rqZFi1a5Ojhu+rVq9u9NplMVK9e3XZO586dA6w3FterVasWv/32GwCnT58GwN/fP0f1uz54TguEIyMj8fT0pHfv3nz++edMnTqVN998k9atW9OtWzd69ux5Sw9lnj17lvLly9vdBAC293rGNoT8+b1LkzZ0wcPDI1/KSxs3P3/+fObPn59lnrCwMNtQI8h8PidOnACyDhKBTNfJxcUl0/vJ29s7yyE7t8LJyYmePXsSGhpKo0aNOH/+fLbDsnL6+5j23q1Ro4ZdPl9fX7y9ve3STp48yd9//20bJ329G827PWnSJCZPnkzHjh254447CAoKol+/flSrVu2G5yy3BwXGIrkUHBzMp59+yrZt2+jatSubNm2iVq1a1KtXz5Zn7dq1TJ48ma5du/LII49QpkwZHBwcePvtt20Bx60yDINvvvmGuLi4TME4WP/gx8bG5ts/2jeTmpqa5cM01/cWAyxdupT58+dz7733MmHCBLy9vTGbzbz22muZHpjKieDgYGbNmsX69esZM2YM69atIzAwkFq1auVo/5z0iHfq1Ak3Nzc2btxIs2bN2LhxI2azmZ49e9ryWCwWTCYTy5Yty/JaXD9jQFbXpqTLLrhNa1dXV1c++ugjdu/ezY8//sjPP//Mhg0b+PTTT1m+fHmW1y0/5ec1/+eff4DMNyG3Kq0HfeTIkbRv3z7LPNcHjdd/upN2nefMmZPlvNbXX9+Cvt4Ad999N6tXryYkJIR69erdtMc+L59QXc9isdC2bVseffTRLLdfH1xn1Lt3b1q0aMHmzZvZvn077733HsuWLSMkJMTuQUe5PSkwFsmlO++8k3LlyrFhwwaaNWvGrl27GDNmjF2eb7/9lmrVqrFw4UK7P/YLFizI8/F/+eUXLly4wPjx4zP1BEdFRfHSSy+xZcsW+vbti5+fHxaLhaNHj2b7wF/aP7iHDx++4T/03t7eWU6xdO7cuRz3pHz77be0atWK1157LVO9S5cubVen/fv3k5ycbDdX7PV8fHzo2LEj69ev5+6772bPnj05muu2SpUqWCwWTp48aXcNr1y5QlRUlN2DWe7u7nTs2JFNmzYxZcoUNmzYQIsWLex67/z8/DAMg6pVq2bZE1uY0j5BSGMYBidPnrQ9NJXWs3v8+PFMvWnHjx+3bU9r07QgMD+YzWZat25N69atmTJlCkuXLmXevHns3r2bNm3a5KqsKlWqsHPnTmJiYux6Q48dO2bbXhBSU1NtD742b948X8pMu9ZOTk65vg7Xl1GmTJlbLuN6eQ1UmzdvTuXKlfnll1+YNGlStvly+vuY9t48ceKE3d+cq1evZurp9vPzIy4u7pavRfny5XnwwQd58MEHCQsLo3///ixdulSB8b+AxhiL5FJab+EPP/zAunXrSElJydRzm9Ybk7EXdP/+/ezbty/Px08bRvHoo4/Ss2dPu6/77ruPGjVq2Mbddu3aFbPZzKJFizKN60yrW7t27fDw8ODtt98mMTExyzxg/Yd3//79dtMn/fDDD1lOBZYdBweHTD3DGzduzDSWr3v37oSHh/PRRx9lKuP6/fv27cuRI0eYM2eObcaAm0n7x23lypV26e+//77d9jS9e/fm0qVLfP755/z111/06tUrU30dHBxYuHBhpvoZhkF4ePhN65Rf1qxZYxvXC9axlpcvX6ZDhw4ABAYGUqZMGVavXm3Xlj/99BNHjx61DcPx9fXlzjvv5Msvv7R9hJ3mVnr3IyIiMqWl3axdPyVXTnTo0IHU1NRM75EVK1ZgMpls55ufUlNTmTlzJkePHmXYsGGZhifcqjJlytCyZUs+/fRTLl26lGl7Tpbbbt++PZ6enrz99tuZZmjIaRnXS5tb+1bnHDaZTLz44ouMGzfuhsObcvr72KZNG5ycnPjwww/t3oPX7wfWGWX27t2b5bR8UVFRpKSkZFmX1NRUu2n4wNo+5cuXv6X3qZQ86jEWyWDbtm22HqeMmjVrZtdD0atXLz744AMWLFiAv79/pp7bjh078t133zF27Fg6duzImTNnWL16NXXq1LGNT7wVSUlJfPfdd7Rp0ybbxQk6d+7MqlWrCAsLo3r16owZM4bFixfzwAMP0L17d5ydnTlw4ADly5fnmWeewdPTkylTpjB16lQGDhxInz598PLy4q+//iIhIcE2N/OgQYP49ttvefTRR+nVqxenTp1i/fr1uVoVsGPHjixatIgpU6bQtGlT/vnnH9avX5+px7lfv36sWbOGWbNm8fvvv9O8eXPi4+PZuXMnQ4YMoWvXrra8QUFB+Pj4sGnTJjp06JCjqc/q1atH//79+fTTT4mKiuLOO+/kwIEDfP3113Tt2pW77rrLLn9QUBAeHh68/vrrODg40KNHD7vtfn5+PPXUU7z55pucPXuWrl274uHhwZkzZ9iyZQv33XcfjzzySI6vU1a+/fbbLBdxaNu2rW3KL7D27D/wwAMMGDDANl1b9erVue+++wBrr+SkSZOYMmUKQ4cOJTg42DZdW5UqVXj44YdtZU2dOpUhQ4bQv39/7r//fqpWrcrZs2f58ccfWbt2ba7qv2jRIn799VeCgoKoUqUKYWFhfPzxx1SsWPGWel47d+5Mq1atmDdvHmfPniUgIIDt27fz/fffM3z48DyvVhkdHW07x4SEBNvKd6dOnSI4OJgJEybkqfzrTZs2jQceeIC7776b++67j2rVqnHlyhX27dvHhQsXWLdu3Q339/T0ZPr06Tz33HMMGDCA3r174+vry7lz5/jpp59o1qzZTef2vp6rqyt16tRh48aN1KhRAx8fH+rWrZvjcedgvTnP+PualZz+Pvr6+jJy5EjefvttRo8eTVBQEH/++Sfbtm2z+8QJ4JFHHmHr1q2MGTOG/v37c8cddxAfH88///zDt99+y/fff5/lmP3Y2FiCgoLo0aMH9erVw93dnR07dnDgwIEs53GX248CY5EMshvqMGvWLLvgrVmzZlSqVInz589nOc53wIABtjl6/+///o86derwxhtvsGnTJn755Zdbrt+PP/5IVFSUbfGArHTq1Inly5fzzTff8NBDDzFhwgSqVq3Khx9+yLx583BzcyMgIMCuB2fQoEGUKVOGd955h8WLF+Po6EitWrXsgqT27dszefJk3n//fV577TUCAwNZunSpLXDOiTFjxhAfH8/69evZsGEDDRo04O233+bNN9+0y+fg4MCyZctYsmQJoaGhfPfdd/j4+NCsWbNM86g6OzvTu3dvPv7445s+dJfRzJkzqVq1Kl9//TVbtmyhbNmyjB49mnHjxmXK6+LiQufOnVm/fj1t2rTJMvgeNWoUNWrUYMWKFbZ5VytWrEjbtm3p3LlzjuuVnenTp2eZvmrVKrvAeMyYMfz999+88847xMbG0rp1a6ZNm2a3st6AAQNwdXVl2bJl/Pe//8Xd3Z2uXbvy7LPP2s2LXK9ePT777DPmz5/PJ598QmJiIpUrV87UY54TnTt35uzZs3z55ZeEh4dTunRpWrZsyZNPPnlLq8CZzWaWLFnCggUL2LBhA1999RVVqlThueees81mkBcXLlywzVPt7u5O+fLladKkCdOnT6dt27Z5Lv96derU4csvv2ThwoV8/fXXRERE4OvrS4MGDRg7dmyOyrj77rspX74877zzDu+99x5JSUlUqFCBFi1a5HoWlDQzZ87klVdeYdasWSQnJzNu3LhcBca5OU5Ofh+feuopnJ2dWb16Nbt376ZRo0YsX76c0aNH2+Vzc3Pjgw8+4O2332bTpk2sWbMGT09PatSoccP3nKurK0OGDGH79u189913GIaBn5+f7cZFbn8m41Y+ExMRKUZee+01vvjiC7Zv337TpZVvV7t37+ahhx5i/vz5dg8GiohIzmmMsYiUaImJiaxbt44ePXr8a4NiERHJHxpKIVJMpKam3vQBGXd390Kbhq24CwsLY8eOHXz77bdERETw0EMPFXWVRESkhFNgLFJMnD9/ni5dutwwz7hx43jyyScLqUbF25EjR5g0aRJlypRh6tSp2U5HJyIiklMaYyxSTCQmJtpWHctOtWrVtPqSiIhIAVFgLCIiIiKCHr4TEREREQE0xjjP9u7di2EYN1y2VkRERESKTnJyMiaTiaZNm94wn3qM88gwjFtaHjUvx0tKSirUY0r+UhuWfGrDkk3tV/KpDUu+wm7DnMZr6jHOo7Se4oYNGxbK8eLi4jh06BB16tTJcnlYKf7UhiWf2rBkU/uVfGrDkq+w2/DAgQM5yqceYxERERERFBiLiIiIiAAKjEVEREREAAXGIiIiIiKAAmMREREREUCBsYiIiIgIoMBYRERERARQYCwiIiIiAigwFhEREREBFBiXOL9c/IUph6fwy8VfiroqIiIiIrcVBcYliGEYLD6wmPOJ51l8YLHWiBcRERHJR45FXQHJuR3ndnAo/BAAh8IPcc+ae/By9gITmNL+M1m/A7afTSYTw+oPo5NfJ+u+YYeY99s8qpSqwrTW02zlz9g5g4uxFzGZTJgxZ1muyXSt7Gs/96jRg27VuwFwIfYCb+15i1JOpXjxrhdt5b574F1ORp3Mvn5ZlNuiQgu61+gOQExSDIv3L8aMmUl3TrKVu/7oeo5EHLnheaf9l3YudXzq0LV6VwAshoXlfywHYGj9obg6ugKw89xO/gn/54bnfX29K7hXIKhakK1uocdCSU5Npkv1LtY2Av6++jf/hP9DclIy5yLOcerUKVxdXO3qd335ns6etKrUylbu/y78j/iUeBqXa4y3izcA52POczzyeHoZN7iuad+dzE4Elg20lXss4hhxKXFUK1XNVm5kYiTnY89n217Z1dvPy89Wblh8GImpiXi7eOPh5AFAUmoSEYkRObquGbd7OnnaXienJpNqpOJodsTRbP0zZhgGqUZqlu0mIiLFyy8Xf+G1w6/xgu8LdKzZsairY6PAuIQwDIOQvSGYTWYshgWAE1Encrx/zxo9bT9HJEaw8/xO6ibUtcvz64Vfc1UmQB2fOrbAODIxkm+OfUMZ1zJ2gfG2M9vYe2lvrso1YbIFxvEp8Xzw5weYTfaB8ZaTW9h6emuuyu1Zo6ddYDx/z3wABvkPsgXG3538ji/++SJX5baq2MouMH5t92tEJ0XTuHxjW2D87YlvWXZgWfpOZ25ebt3Sdfnqnq9sr/+z8z+ciDrBip4raF6hOQDfn/qe1//3eq7qW8a1DD/e/6Pt9fSd09l7aS/zOs6zXZ/tZ7fz/M/P56pcs8nM/of229V36+mtvHTXS9wXcB8Av138jVGbR+WqXID/G/x/tqD9tV9e44t/vmBsk7GMaTwGgH/C/2Hg+oFZ7pvdjZIJE1/c8wU1vWsC8Pb+t1n+x3LuC7iPZ1o8A8CV+Cv0XdPXfn8gNSUVx8OOmM3mzOWaTMwNmkvDcg0BWHtkLUv3L6VjtY4839J6TVMtqfRb2y/bG4JMNyHX0ic2n2i7WdpxbgeL9i3ijjJ38EKrF2znO/b7scQmx97wvK+/uXmg3gO0r9oesN7ELdy3kCqeVZjccrKt3Nm/zOZS3KXsy83iXLr6dbXdlF+IvcDS/Usp5VzKdn0BVh5cyeno05luam50U9akfBM6+3UGIDY5lvcOvIfJZOLJpk/ayt10fJP9TeO1/VOSU7h8+TK/mH7B2cnZrvxaPrXoULUDYP0b8fGhjzGZTAz0H4iLgwtgvUE9FnEsR+2VllbWrSxtKrex1W3LyS0kW5JpX6U9ns6eABwJP8LxqOM3bK/rbyY9nTxpUr6Jrdz9l/eTkJJA/TL1bX97LsZe5EzMmRxd17RjOpmdqFs6/d+I09GnSUhJoJJHJVt9Y5JiuBx/OVfvBxMmKnhUsJUbnRRNUmoSHk4etr/ByZZk4pLjbnhdE1ITSLIkkZSahFOqE5jA0eSom+ES4vpPwINqBBWbtlNgXELsOLeDg2EHM6U/3uhx6pWph4GB9f9r/xm2n8CABmUa2Pap41OH19q9ZvujmWZ8s/HEJMVk2j9tyMb1aQYGjco2su1fzr0ck1pMws3Rza7cQf6DbP/Q2Mq4rn7Xl9u4XGPb/m6ObowIHGH7o5imY7WOVClVJb1+Gcu99v36et9R5g7b/iZM9KvTD8MwcDI72dIblW1EfEp8ehlZ1O/6c/H39berW5vKbYhLjsPd0d2W5uflR+tKrUlNTSUmNgZ3D3fMZnOmemc8l2qlqtmVW7d0XTycPGy9rwClXUsTUDrgpu2VVq7FsFDatbRduWXdylLJo5LtHyYAF0cXyrmVu+H1vP4aXf+HzcnBCRcHFxxMDunX3WTCweSQqdzcyO0woozX4FpClhJTE4lLiSMpNcmWZjEsRCVFZb1DavbHTLKklxGdFM2ZmDOExYfZ1Sm3N6JpZaUJTwjn98u/4+Zg/zu399Jeu3w50alaJ9vPVxOu8uPpH+0CI4D/O/t/nIw6matyq5WqZguMIxMj+fLwl5R1K2sXGH9/6vtc3zw/kPqAXWC87MAyzCazXWC88fjGG988X8yc1KtGL7vAOO2ms0+tPrbA+Jtj3/Dl4S9zVd9WFVvZBcYv73iZ6KRo1vVbZws0NxzfYH/znAPX3zy/+H8vcjLqJCt7rqRZhWYAbD65Odc3z2XdyvLDfT/YXr/w8wvsu7zP7ub5pzM/MfnnydkVkSUHkwP7Htpnez31/6Zmunn+34X/MXrz6JwV+Gf6jxlvnmfsnMEX/3zBuCbjGN3YWtY/4f9w//r7c3TDkTHI/yT4E6p7VQesn4B+8OcHDKg7gAnNJgDWT8buW39frj61M2FidofZtn+TQo+F8t6B92hfpT0TW0wErDfPQ74ZYju/nH5q92TTJ2lRsQUAu8/v5p3f36FBmQZ2v3MTf5xIXHJctp9WZpU+yH8Qbaq0sV3LZb8vo5JnJSY2n2grd+6vc7kSf+WG5w1wMe6i3SfgO87toG2Vtjlr8wKmwLgESOstNmGyCyJMmNh2dhuPN3k8V3da5dzLcXftuzOlp/X83ipfV1+G3zE8U3pWx8oNT2dPu1+8NP3r9s9TuQ5mB15p+0qW5ea17P8G/TdTWr86/ehXpx9xcXEcOnSI+vXr4+7unsXe2ZvbcW6mtOBawQTXCr7lumZXbhe/LnTx65KncrO6DndVusvuH8Y0N7tpcjY72/JOaTWFZ+981u6GprZPbX6+/+dsb2KALNPLupe1lTH8juH0r9MfD2f7G4+1fdfaBdfxCfEcPXaUmjVr4urqmmW9a3jXsJXRo0YPAssG4uPiY0tzMDnwfo/3c3TDkTEt401u8wrNmd9pfqYbnZltZ5JsSbYvI5sbpaxuRmt512Ja62mZbp7HNB5DdFJ0tjeIWZ1Ls/LNbPuXcSvDuCbjcHeyf9/3r9OfVpVaZXuDmOn9YBg0rdDUtr+boxsP1Hsg09/BtlXaUtatbKbzTk5JJjw8HG8fbxwcHOzq3ahcI7syetXohYH9zXODMg2ITIy84fvVYljs0ur51rMrt3mF5sQlx9l1JFT2rEyz8s1ueN7Xvx/8SvnZlVvdqzpOZie7cr1cvKjhVcO+jBvd7BoGvm6+duV6uXjh6+qLs0P676GT2YlSzqVy1HmQ9l/Gm+SM8ru3MKubZ8MwSDFSsr0xzk7ap7QAcclxXE24ag0qr0k1UrkUfynXdUxMSbT9HJEQwZGII9T1Sb8ZtWDh0NVDuS43MjHS9vOV+Cv8cuGXTNd317ldRCfn7ua5deXWduVuOrEJ/9L+dv8+bz29Ndc3z2aTmZC9IbSp3KZY9BqbDD3BlScHDhwAoGHDhgV2jKTUJLp/0Z2whLBM28q4luG7gd/Z/bGS4i0vgbEUD2rDkk3tV/wYRvonThbDQqolNVOwnfGGKTYulr///puAgABc3VwxDINSzqUwm6xzCsQmx5KQkoCbo5vtRizZkmz71CbLG1Fr4ZnSq3pWxcnBemN0Jf4K4QnheLt4U969vK3cw+GHbTcxaXW82c2of2l/640F1mFGJ6NO4uvqa/ukxmJY2HluZ45uODLWu1HZRrbhKudizrHv0j7KuJWxe15l4/GN1pvnm9yMZvwEsnmF5tQpXQewPtvy/anv8XH1oU+tPrZyvz78dfpN47UbxOvrfDLqJOuPrc/0HljadWmB9hrnNF5Tj3EJ4OzgzOo+q7macJWEhASOHz9u66m6/g5eRESkpMnYU2g2mTE73HjSLJOTCXcHdzydPHF3znxzc/2QM7D2cFf0qJinepZ1K0tZt7J2aU5mJ7tPcm5FRY+KmepmNpnzHChW9qxMZc/KmdJ71eyVp3IreVZiaIOhmdJv9mmrYRgM+WZIlp+AF5deYwXGJUTaL01cXBzGBYN6peupp0NERERKjGRLMhdiL9gFxWDtqb4Qe4FkS3KRd/YpMBYRERGRAlcSPgFXYCwiIiIihaK4fwKule9ERERERFBgLCIiIiICKDAWEREREQEUGIuIiIiIAAqMRUREREQABcYiIiIiIoACYxERERERQIGxiIiIiAigwFhEREREBFBgLCIiIiICKDAWEREREQEUGIuIiIiIAAqMRUREREQABcYiIiIiIoACYxERERERQIGxiIiIiAigwFhEREREBFBgLCIiIiICKDAWEREREQEUGIuIiIiIAAqMRURERESAYhgYHz16lBEjRtCkSRPatm3LnDlzSEpKuuE+ly5dYs6cOfTt25emTZvSoUMHnnnmGc6ePZsp76+//sqwYcO48847adWqFY8++iiHDh0qqNMRERERkRKiWAXGkZGRDB8+nOTkZEJCQnj66af57LPPmD179g33O3jwIJs3b6ZXr14sXryYyZMn888//zBo0CCuXr1qy3fs2DEeeeQR3N3defPNN3n11VeJjIzk4Ycf5vLlywV9eiIiIiJSjDkWdQUyWr16NbGxsSxcuBAfHx8AUlNTmTFjBqNHj6ZChQpZ7te8eXM2btyIo2P66TRr1oyOHTuyZs0aRo4cCcCWLVswDIP58+fj6uoKQEBAAF27dmX79u3069evQM9PRERERIqvYtVjvG3bNlq3bm0LigF69eqFxWJh+/bt2e7n5eVlFxQDVKxYEV9fXy5dumRLS05OxtnZGRcXF1taqVKl8u8ERERERKTEKlaB8bFjx6hVq5ZdmpeXF+XKlePYsWO5Kuv48eOEhYVRu3ZtW1pwcDCpqam89dZbhIeHc/HiRWbNmkWlSpXo0qVLvpyDiIiIiJRMxWooRVRUFF5eXpnSvb29iYyMzHE5hmEwc+ZMypcvT3BwsC29Ro0arFixgieeeIKlS5cCUKVKFd5///089RwbhkFcXNwt758b8fHxdt+l5FEblnxqw5JN7VfyqQ1LvsJuQ8MwMJlMN81XrALj/BISEsKuXbt49913cXd3t6UfP36cJ598krZt29KvXz8SExNZvnw5jz32GKtXr6Zs2bK3dLzk5ORCn9nixIkThXo8yX9qw5JPbViyqf1KPrVhyVeYbejs7HzTPMUqMPby8iI6OjpTemRkJN7e3jkq47PPPmPRokW8+uqrtG7d2m7bvHnzKFu2LHPmzLGltWzZkk6dOrFq1SomTpx4S/V2cnKiTp06t7RvbsXHx3PixAlq1KiBm5tboRxT8pfasORTG5Zsar+ST21Y8hV2Gx45ciRH+YpVYFyrVq1MY4mjo6O5fPlyprHHWdm8eTPTp09n/PjxDBw4MNP2I0eO0KRJE7s0Dw8P/Pz8OHXq1C3X22Qy2fVMFwY3N7dCP6bkL7Vhyac2LNnUfiWf2rDkK6w2zMkwCihmD9916NCBHTt2EBUVZUvbtGkTZrOZtm3b3nDf3bt3M3HiRAYNGsTYsWOzzFO5cmUOHTqEYRi2tJiYGE6ePEmVKlXy5yREREREpEQqVoHx4MGD8fDwYOzYsfzf//0fX375JXPmzGHw4MF2cxgPHz6cbt262V4fPXqUsWPHUqNGDfr27cu+fftsXxl7ggcPHsyff/7JpEmT2LZtG1u2bGHUqFEkJSUxaNCgQj1XERERESleitVQCm9vb1auXMkrr7zC2LFj8fDwYODAgTz99NN2+SwWC6mpqbbX+/fvJzo6mujoaIYMGWKXt3///raV87p27cpbb73Fe++9x9NPP42TkxMNGjRg1apV1KhRo8DPT0RERESKr2IVGAPUrl2bFStW3DDPBx98YPd6wIABDBgwIEfl9+rVi169et1q9URERETkNlWshlKIiIiIiBQVBcYiIiIiIigwFhEREREBFBiLiIiIiAAKjEVEREREAAXGIiIiIiKAAmMREREREUCBsYiIiIgIoMBYRERERARQYCwiIiIiAigwFhEREREBFBiLiIiIiAAKjEVEREREAAXGIiIiIiKAAmMREREREUCBsYiIiIgIoMBYRERERARQYCwiIiIiAigwFhEREREBFBiLiIiIiAAKjEVEREREAAXGIiIiIiKAAmMREREREUCBsYiIiIgIoMBYRERERARQYCwiIiIiAigwFhEREREBFBiLiIiIiAAKjEVEREREAAXGIiIiIiKAAmMREREREUCBsYiIiIgIoMBYRERERARQYCwiIiIiAigwFhEREREBFBiLiIiIiAAKjEVEREREAAXGIiIiIiKAAmMREREREUCBsYiIiIgIoMBYRERERARQYCwiIiIiAuQxMJ42bRp79uzJr7qIiIiIiBSZPAXGoaGhPPjgg3Tt2pUFCxZw4sSJfKqWiIiIiEjhylNgvHPnTubOnUvdunV555136NWrF4MGDeKjjz7i6tWr+VVHEREREZECl6fA2NnZmV69erFkyRK2b9/OtGnTcHZ2ZubMmXTo0IHRo0ezYcMGEhMT86u+IiIiIiIFwjG/CvL29mbw4MEMHjyYc+fOMWfOHDZt2sS2bdvw8PCgR48eDBs2jHr16uXXIUVERERE8k2+BcYA58+fZ/369axfv57Dhw/j4+ND7969cXJyYt26dXz99ddMnTqVBx54ID8PKyIiIiKSZ3kOjKOioti4cSPr169nz549ODg40LFjRyZMmEBQUBBOTk4APPPMM0ycOJHFixcrMBYRERGRYidPgfHYsWPZtm0bycnJNG7cmKlTpxIcHIy3t3emvM7OzvTo0YMtW7bk5ZAiIiIiIgUiT4HxoUOHeOSRR+jXrx81atS4af62bduyatWqvBxSRERERKRA5Ckw3rp1a67y+/r60rJly7wcUkRERESkQORpurbTp0/fMDjeunUrZ86cycshREREREQKRZ56jOfMmUNMTAydO3fOcvtHH32El5cX8+bNy8thREREREQKXJ56jPfu3UubNm2y3d66dWt+/fXXXJV59OhRRowYQZMmTWjbti1z5swhKSnphvtcunSJOXPm0LdvX5o2bUqHDh145plnOHv2bJb5f/zxRwYPHkyTJk248847GTZsGBcuXMhVPUVERETk9pKnHuOoqCg8PDyy3e7u7k5ERESOy4uMjGT48OHUqFGDkJAQLl68yOzZs0lISODll1/Odr+DBw+yefNm7r33Xho3bkx4eDhLlixh0KBBhIaG4uvra8u7du1aXnzxRUaOHMlTTz1FbGwsv/76q1bnExEREfmXy1NgXKlSJfbs2ZPtvMS//fYbFStWzHF5q1evJjY2loULF+Lj4wNAamoqM2bMYPTo0VSoUCHL/Zo3b87GjRtxdEw/nWbNmtGxY0fWrFnDyJEjAYiIiOA///kPL7zwgl2du3TpkuM6ioiIiMjtKU9DKfr06cM333zDqlWrsFgstvTU1FRWrlzJhg0b6NOnT47L27ZtG61bt7YFxQC9evXCYrGwffv2bPfz8vKyC4oBKlasiK+vL5cuXbKlbdy4EYvFwsCBA3NcJxERERH5d8hTj/Ho0aP57bffeO2111i6dCk1a9YE4Pjx41y9epWWLVvy+OOP57i8Y8eOce+999qleXl5Ua5cOY4dO5aruh0/fpywsDBq165tS9u/fz81a9ZkzZo1LFmyhIsXL1K3bl0mTpxIUFBQrsoXERERkdtLngJjZ2dnli9fztdff83mzZs5deoUAI0aNaJ79+7069cPsznnndJRUVF4eXllSvf29iYyMjLH5RiGwcyZMylfvjzBwcG29MuXL3P8+HHmz5/Ps88+S7ly5fjoo4944oknWLNmDXXr1s3xMa4/Xlxc3C3tm1vx8fF236XkURuWfGrDkk3tV/KpDUu+wm5DwzAwmUw3zZenwBjAbDZz7733ZurpLUohISHs2rWLd999F3d3d1t6WgD73//+1zauuGXLlvTo0YNly5YxZ86cWzpecnIyhw4dype659SJEycK9XiS/9SGJZ/asGRT+5V8asOSrzDb0NnZ+aZ58hwY5ycvLy+io6MzpUdGRuLt7Z2jMj777DMWLVrEq6++SuvWrTOVD3DXXXfZ0pycnLjzzjs5fPjwLdfbycmJOnXq3PL+uREfH8+JEyeoUaMGbm5uhXJMyV9qw5JPbViyqf1KPrVhyVfYbXjkyJEc5ctzYHz58mW++OIL/vzzT6Kjo+0ewgMwmUysXLkyR2XVqlUr01ji6OhoLl++TK1atW66/+bNm5k+fTrjx4/P8gG7GwWveZmuzWQy2fVMFwY3N7dCP6bkL7Vhyac2LNnUfiWf2rDkK6w2zMkwCsjjrBR//fUXwcHBLFmyhFOnTrF7927Cw8M5efIkv/zyCxcuXMAwjByX16FDB3bs2EFUVJQtbdOmTZjNZtq2bXvDfXfv3s3EiRMZNGgQY8eOzTJPp06dANi5c6ctLSkpif/973/ccccdOa6niIiIiNx+8tRj/Oabb+Lu7s6aNWtwdXWlTZs2vPDCC7Ru3ZqNGzcyffp0/vvf/+a4vMGDB/PBBx8wduxYRo8ezcWLF5kzZw6DBw+2m8N4+PDhnDt3js2bNwPW1fLGjh1LjRo16Nu3L/v27bPl9fX1xc/PD4A77riDHj168NJLLxEREUG5cuX4+OOPuXLlCo888kheLoWIiIiIlHB5Coz37NnDo48+SuXKlW0r3KX1EPfq1YvffvuNOXPm8OGHH+aoPG9vb1auXMkrr7zC2LFj8fDwYODAgTz99NN2+SwWC6mpqbbX+/fvJzo6mujoaIYMGWKXt3///syePdv2evbs2cydO5c333yTmJgY7rjjDt5//30CAgJu5RKIiIiIyG0iT4GxxWKhbNmygPXBNgcHB7sloAMCAvjyyy9zVWbt2rVZsWLFDfN88MEHdq8HDBjAgAEDclS+u7s7U6dOZerUqbmql4iIiIjc3vI0xrhq1aqcOXPGWpDZTNWqVe3G7+7Zs4dSpUrlrYYiIiIiIoUgTz3G7dq1Y9OmTbahDkOGDGH27NmcPn0awzD45ZdfGDFiRL5UVERERESkIOUpMB4zZgzBwcEkJyfj5OTE8OHDiYuL47vvvsNsNvPEE08wevTo/KqriIiIiEiBueXA2DAMHBwcqFu3Lk5OToB1jrgnnniCJ554It8qKCIiIiJSGG55jHFycjItW7Zk1apV+VkfEREREZEiccuBsbOzM2XLls3RutMiIiIiIsVdnmal6N+/P2vXriUpKSm/6iMiIiIiUiTy9PBdQEAA33//PX369KF///5UqVIFV1fXTPm6d++el8OIiIiIiBS4PAXGEydOtP08f/78LPOYTCYOHTqUl8OIiIiIiBS4PAXGevBORERERG4XeQqMW7ZsmV/1EBEREREpUnl6+E5ERERE5HaRpx7jhx566KZ5TCYTK1euzMthREREREQKXJ4CY8MwMqVZLBbOnTvH+fPnqV69OuXLl8/LIURERERECkWeAuMPPvgg220//PADL730ElOmTMnLIURERERECkWBjTHu1KkT99xzD6+99lpBHUJEREREJN8U6MN3fn5+HDhwoCAPISIiIiKSLwosME5JSWHjxo2ULl26oA4hIiIiIpJv8jTGOLvxw9HR0ezbt48rV64wefLkvBxCRERERKRQ5Ckw3r17d6Y0k8mEt7c3zZs3Z9CgQbRr1y4vhxARERERKRR5Coy3bt2aX/UQERERESlSWvlORERERIQ8BsahoaE3HEM8ZcoUNmzYkJdDiIiIiIgUijwFxitWrMDZ2Tnb7S4uLloOWkRERERKhDwFxsePH6d+/frZbq9Xrx7Hjh3LyyFERERERApFngJjwzCIjo7OdntUVBQpKSl5OYSIiIiISKHIU2DcoEEDQkNDSUpKyrQtKSmJ9evX37BHWURERESkuMhTYPzYY49x+PBhHnroIbZu3crp06c5ffo033//PcOGDePIkSOMGjUqv+oqIiIiIlJg8jSPcVBQEK+++iqvvvoqY8eOtaUbhoGHhwevvPIKHTt2zGsdRUREREQKXJ4CY4ABAwbQvXt3tm/fzqlTpwDw8/Ojbdu2eHp65rmCIiIiIiKFIc+BMYCnpyc9evTIj6JERERERIpEnsYY79ixg7lz52a7fd68eezcuTMvhxARERGR28imP87Tb+kvDP7yAv2W/sKmP84XdZVs8hQYL168mPPnsz+ZixcvsmTJkrwcQkRERERuE2v3nWXMh3s4fCmWZAscvhTLmA/3FJvgOE9DKf755x969uyZ7faGDRvyww8/5OUQIiIiIlJMGYZBdGIKV6ITuRKTxJWYROtXdCKXY5IIS3t9bVtcUqp1v7T9AZMJ5n9/mJ6BlYrsPNLkKTBOSkoiOTn5htsTEhLycggRERERKUSGYRAZn8yVmEQuR2cIdmMSuWL32vpzYoolj8eDY5dj86n2eZOnwLhu3bps3ryZESNGZNpmGAbfffcdtWvXzsshRERERCSPLBaD8Lgku17dy9f38l4LfMNiE0lONW5eaAYezg6ULeVCWU8Xyno6X/vuQtlSLpTL8HrUB79y+GIMGUs3maBWOY/8PeFblKfAeOjQoTz//POMHz+esWPH2oLgI0eOsHjxYvbt28drr72WLxUVERERkXQpqRauxiVd14t7LdiNTuRyhl7dq7FJpFpyF+x6uTragt1yGQPeLAJgN2eHHJU5sZs/Yz7cg4lrwyiw9hhP6OKf6/MvCHkKjPv27cvp06dZvHgxmzdvxmy2PstnsVgwmUw8/vjj9O/fP18qKiIiInK7S0qxEBabPmThchZDGMLSgt24JIzcxbqUdney681NC27LebpQtlR6oFvG0xkXx5wFu7nRM7ASS4c2Y97mvzl2OZZa5Tx4uls9egZWzPdj3Yo8z2M8btw47rnnHjZv3szp06cB6wIfXbt2xc/PL88VFBERESnJEpJT7XpyM/bsXr72oFra68j47J/dyorZBL4eGYYuZNOrW66UC74ezjg55GlCsnzRM7ASHWp5c+jQIerXr4+7u3tRV8kmXxb48PPz45FHHsmUfvXqVTZs2MDQoUPz4zAiIiIixUJcUgpXopPSe3QzPZiWHghHJ6bkqmwHs4kyHs52vbrlbL28GYNga7DrYDYV0Fn+++RLYJxRfHw8W7ZsYf369ezYsYPU1FQFxiIiIlKsXT/tWNo0Y5fTHk6Ltp+JIW3asZxydjBbe2+zeUAtY/Dr7eaEWcFukciXwNhisfDzzz+zfv16vv/+exISEvDz82PYsGF07tw5Pw4hIiIikisFPe2Yq5PZrve23HW9uRkDYS9XR0wmBbvFXZ4C43379rF+/Xo2btxIeHg4lStXJiEhgf/85z8MGjQov+ooIiIiAlinHbsam8SpyGQijl0lJiW8SKYdK1vKBQ9nBwW7t5lcB8bHjh1j/fr1hIaGcvr0afz8/Bg0aBB9+vTB2dmZHj164O3tXRB1FRERkdvQrU87Fpaj8q+fdqyMp3OmXt20YQw5nXZMbk+5DoyDg4MpW7Ysffr0oVevXjRq1Mi27dSpU/laORERESmZspt2LMxu2eBbn3aslLOJ8t5ulC/lViTTjsntKdeBsaOjI1FRUZw9e5YLFy5Qr149nJ2dC6JuIiIiUowUl2nHXE0pHPnn72I31ZeUfLkOjHfs2MGmTZtYt24dEyZMwN3dnS5dutCnTx+qVKlSEHUUERGRAlISpx2Li4u7lVMVualcB8alSpVi0KBBDBo0iPPnz9vGG69btw53d3dMJhPHjh0jKSlJPckiIiKFzDAMYhJT0h9Giy7kacc80rf5aNoxKWHyNCtFpUqVGDVqFKNGjeKvv/5i3bp1bNiwgbfeeot33nmHtm3b0rlzZy0LLSIikgeadkykcOTbAh/16tWjXr16PPfcc+zevZt169axefNmtmzZosBYRETkOhaLQXhckt00Y5p2TKRo5fvKdwCtWrWiVatWTJs2jZ9++smWHhcXx/Lly+nXrx9Vq1YtiEOLiIgUmVufdixnSrk6ZjtGV9OOieRdgQTGaZydnenWrZvtdVxcHIsWLaJ58+YKjEVEpERITrXYphhLn3kh6+EMtzLtWGl3p0xLA18/7VgZTxfKeDjj6qRgV6QgFWhgnBUjt38xRERE8llSqsHZiARiw4p22jFfD2ecHMwFdJYikluFHhjfzNGjR5k5cyZ79+7Fw8ODvn378tRTT91whotLly6xYsUKtm/fzqlTpyhVqhR33nknEydOzHYKOYvFwsCBAzl48CDz58+nZ8+eBXVKIiJSCHI67djl6ARiElOBizkuu6CmHROR4qVYBcaRkZEMHz6cGjVqEBISwsWLF5k9ezYJCQm8/PLL2e538OBBNm/ezL333kvjxo0JDw9nyZIlDBo0iNDQUHx9fTPts3r1ai5ezPkfRRERKVwFPe2Yk4Pp2nAFl+uC3gyBr6YdE/lXKVaB8erVq4mNjWXhwoX4+PgAkJqayowZMxg9ejQVKlTIcr/mzZuzceNGHB3TT6dZs2Z07NiRNWvWMHLkSLv8V69eZf78+Tz33HO88MILBXY+IiJiL7tpxzIuE3w5w6pqBTHtmKejQdjZ4zRv1AAPD48COlMRKYmKVWC8bds2WrdubQuKAXr16sW0adPYvn07AwYMyHI/Ly+vTGkVK1bE19eXS5cuZdo2d+5c28wZIiKSNyVt2rG4uDgSLps1PZmIZFKsAuNjx45x77332qV5eXlRrlw5jh07lquyjh8/TlhYGLVr17ZL//333wkNDSU0NDTP9RURuV1p2jER+TcqVoFxVFRUlr2/3t7eREZG5rgcwzCYOXMm5cuXJzg42JZusViYMWMGI0aMoGrVqpw5cyZf6m0YRqGt2x4fH2/3XUoetWHJV1LbMDnVwtXYZMJirb27YbHXvmKSuHItPe11eFwyuZ1DyMfNiTKeTpT1cKaMhzNlPK9993C69j0tzQkXx5wFu0ZKInEpuT/XGymp7Sfp1IYlX2G3oWEYOfqUqFADY19fX77//nvKlStXoMcJCQlh165dvPvuu7i7u9vSP//8c65cucKoUaPy9XjJyckcOnQoX8u8mRMnThTq8ST/qQ1LvuLQhkmpBhEJFiITU63fEyxEJF77nmAhMtFCZEIqEYkWYpJyF+qaAC8XM96uZnxczPi4pv/s7epgfX0t3cvFjGOmh9NSrn0BqUAUREZBzrs5ClZxaD/JG7VhyVeYbXijGc7S5CowXrhwYa4rYTKZGDt2LABmsznb6dPAOmwiOjo6U3pkZCTe3t45Ot5nn33GokWLePXVV2ndurUtPTY2lrlz5/L000+TnJxMcnIyMTExACQkJBATE4Onp2duTs3GycmJOnXq3NK+uRUfH8+JEyeoUaMGbm5uhXJMyV9qw5KvoNswLik1Q09uei9uWBa9vdZpx3LOwWTC18OJMp7O6T27116X8biWdu3n0u5Ot+W0Y/odLPnUhiVfYbfhkSNHcpSvUAPjm6lVq1amscTR0dFcvnyZWrVq3XT/zZs3M336dMaPH8/AgQPttoWHhxMREcG0adOYNm2a3bbnn3+esmXLsn379hzV83omk8muZ7owuLm5FfoxJX+pDUu+nLZhQU875uxgtga62SwmoWnHsqbfwZJPbVjyFVYb5vRh21wFxn/99dctVSanOnTowNKlS+3GGm/atAmz2Uzbtm1vuO/u3buZOHEigwYNyjIQL1euHKtWrbJLu3LlChMnTuTJJ5+kTZs2+XciInLbS5t27FxMTIZZF+xnYijoacfKlnKhrIcLXm6OmmFBRCQfFKuH7wYPHswHH3zA2LFjGT16NBcvXmTOnDkMHjzYbg7j4cOHc+7cOTZv3gxYV8sbO3YsNWrUoG/fvuzbt8+W19fXFz8/P1xcXDJNz5b28F2dOnVo1qxZwZ+giBRrOZ127HJUImExiaQYuVskKL+nHRMRkfxVrAJjb29vVq5cySuvvMLYsWPx8PBg4MCBPP3003b5LBYLqanpHzXu37+f6OhooqOjGTJkiF3e/v37M3v27EKpv4gUP6kWg7DYrJcG1rRjIiKSUZ4D47/++osPP/yQP//8k+joaCwW+48LTSYTW7ZsyXF5tWvXZsWKFTfM88EHH9i9HjBgQLaLf9xI1apV+fvvv3O9n4gUreRUi22ltMtZDGFIW0ziSkwiV+OSMHI575iPu1OmXt1ypdJfezoahF84RavG9SntVapgTlJERApdngLj3bt38+ijj+Lt7U1gYCB//vknd911F4mJiezbt486deoQGBiYX3UVkdtYQnJqpqWBr8QkXRvKYP9wWkRccq7KNpmwzrjg6ZLtA2ppwa+vhzNODuYblhcXF8ehKIccz8UrIiIlQ54C4wULFlCtWjU+++wzkpKSaNOmDaNHj6Z169bs37+fxx57jEmTJuVXXUWkhIlLSuFKdNK14Qr2PbnXD2mITszdKg4OZlN6sFsqw+wLWQxp8PVwvi2nHRMRkfyVp8D4zz//5Mknn8TT09O2Ml3aUIrGjRtz//33M3/+fIKCgvJeUxEpcgU97ZiTg+mGvbqadkxERApSngJjBwcHPDw8AOviHI6OjoSFhdm2V6tWjaNHj+athiJSoAzDICo+5bpe3YKfdqyMh3OGoNf6gJqmHRMRkaKUp8DYz8/PtpSfyWSiVq1abNmyhXvuuQeAH3/8kbJly+a5kiKSO9lNOxYWm5SpVzcsJomk1NwFu5p2TEREbkd5CoyDgoL48ssveeaZZ3B0dGTEiBFMmTKF7t27A3Dq1CkmTpyYLxUV+bfTtGMiIiIFK0+B8RNPPMFDDz2Eg4P1H8n+/ftjNpv57rvvcHBwYMyYMbc0jZrIv0VRTzuW1qtbxsMZVycFuyIi8u+Wp8DYycmJ0qVL26X17duXvn375qlSIiVZQnLqdUMW7KcduxQZz7mr0USHXiEyPnczMeR02rGyni6U8bz5tGMiIiKSLk+B8Zw5c+jTpw8NGjTIr/qIFEs5mXYsLMa6PTpB046JiIiURHkKjD/88EPef/99qlWrRu/evenVqxcBAQH5VTeRAlOU0455OUNs2Hma1q9LtXLemnZMRESkmMhTYLxjxw62bNnChg0bePfdd3n77bepVauWLUiuVatWftVT5KYKetoxF0dz1jMveOZu2rG4uDgOHbpK3fIeuLs758epi4iISD7IU2Ds6elJv3796NevH1FRUXz77bds2rSJJUuWsHDhQvz9/QkODmbUqFH5VV/5l8k47VhYjP3MC5p2TERERPJTngLjjLy8vBg0aBCDBg0iPDyctWvXEhISwrx58xQYix1NOyYiIiLFUb4FxgDJycls27aNDRs28MMPPxAXF0elSpXy8xBSTGnaMRERESnp8hwYp6SksH37djZs2MD3339PTEwM5cqVY8CAAfTu3ZtmzZrlRz2lCCSmpNp6cbOadizjMIaIuORclZ1x2rEynjfu1dW0YyIiIlIY8hQYv/DCC3z//fdERkZSunRpgoODCQ4O5s4779RYzAKw6Y/zzP3ub45fiaVm2Wgmdg+gZ2DueuSzmnYsLGPPbnR6r6+mHRMREZF/kzwFxt9//z1du3ald+/e3HXXXbYV8CT/bfrjPGM+3IMJMIDDl2IZ8+EeljzYjHZ1yxbJtGO2wPfaa007JiIiIiVZrgPj33//HT8/P3x8fNi+fTuOjtkXcfr0aX777Tf69euXlzoK8NaWw7agmAzfn/hoD7kcrptv046JiIiI3E5yHRjff//9zJkzh7vvvhtHR0ciIiIICgpi2bJltGzZ0i7v3r17mTJligLjfHD8SmyWAXBaWrbTjmXRy+vpomBXRERE5Hq5DoyN66YTMAyDxMREUlNz9xG95E7Nsh78fSHaLjg2AbXLebD+yfaadkxEREQkj/SofwnxVNe6GFiDYa59N4BJPeopKBYRERHJBwqMS4iegZVYOrQZ/hU8cDKDfwUPlg5tTs/AikVdNREREZHbQr4u8CEFq2dgJTrU8ubQoUPUr18fd3f3oq6SiIiIyG3jlgLjs2fPcvDgQQCio6MBOHnyJF5eXnb5zpw5k8fqiYiIiIgUjlsKjOfPn8/8+fPt0mbMmJEpn2EYmv1AREREREqEXAfGs2bNKoh6iIiIiIgUqVwHxv379y+IeoiIiIiIFCnNSiEiIiIiggJjERERERFAgbGIiIiICKDAWEREREQE0AIfIiIiIlJYIk5DXBimhATcIo5jupAMrq7gXgZ8qhV17RQYi4iIiEghiDgNC5tDSiJuQAOAn69tc3SBcb8VeXCsoRQiIiIiUvDiwiAlMettKYnW7UVMPcYiIiIiUnAMAxIi4Mrhoq7JTSkwFhEREZFbZxgQHw4Rp9K/yvqDf3fr9qvHIKRZ0dYxhxQYi4iIiEj2DANir4AlBbwqWdNiw2DtE+mBcFKM/T6NH0gPjL2rgskMLl7WnuNiTIGxiIiIiFjH+f4VmqHn93T6zynx0ORB6LfYmtfFE/7ZZL+/ZwXw8bN++bVKT3d0gRfOweW/4Z2gwjufW6DAWEREpBhJTU0lOTm5qKtRrCUmJtq+m82aR+CmLBaIuwJR5yE67euc9XX5+tB6rDVfciJsmpZ5f9ey18pxgISE9PS+71mnWfOqDJ4VwcnFfr+MeTGBozd414bUpMzHcHC2brfbJ2ecnJxwcHDI9X5ZUWAsIiJSDBiGwYULF4iIiCjqqhR7FosFR0dHzp07p8AYrEMdjFSwpFqHO5jM4OSWvi3yDGBcy+wGDrXApxb4AI6ucPx4elkdl4DJBGZHMDuA6dp3s6M1PWNe1zvAAkRYIOJczurafSUYFgzDICUlBUdHR0wmk7XOYQkQdvzmZWTBx8eHihUrWsvKAwXGIiIixUBaUFy+fHnc3d3z/A/87Sw1NZXExERcXFzyraewWDMMa9Dr4Jj+OvocpCZbvywppAe+ZnByh9I10ve/nAJGCpidwMEp/buDEzi4gLN7hoPVLJRTyq82NAyDuLg4Ll26BEClSpXyVC8FxiIiIkUsNTXVFhSXKVOmqKtT7KWmpgLg6up6ewXGidHWYQYpSdbvGb+cPaFs3fS8kXHXgl2urUphvhboOoOzh3U1uTSV66f3+BYT+dmGbm7W3vFLly5Rvnz5PJWnwFhERKSIpY0pdnd3v0lOKZEMS9YBr9nROmNDmvAT13p/s5B63bjzUpWtga6D87Uvp+wDXwenfDmN4iztdyc5OVmBsYiIyO1AwydKKIslPdjFAFfv9G2X/rLO6JAVB2f7wNjZ0zpW2BbsXvtydLYOf8jIQ58sZJRfvzsKjEVERERuxLBYHw5LE30BkuPTg+GMvbwOzvaBcVrAZjJnHfBm5Fs443sle3qUU0RERPIsICDgpl9fffVVodQlJCSEpk2b5m6n5ARIiITYyxB5Fq4et867e+GAtdc3o4RI60IVyXHpQbHJDI6uBHQYQEBAAJ988ok1vXQNqBAIFRux/fBVAtr0JqBVV85EJoNb6TydZ1RU1C1d16+++oqAgACuXr2ap+PfjtRjLCIiInn26aef2r2+//77GTZsGH369LGl+fn5FXa1rCyp6b27aeN8DQv4VEvPE3HSGuhmKdU6E0Ra769H2WuzRGTo/TU72La7u7uzYcMGhgwZYl3c4prQ0FDc3d2Ji8vuOFLUFBiLiIhInjVp0iRTWqVKlbJMz1dpc/imXBvS4OqVvi38BCREWbdnYrq2VPG1YNfJzVqWo/NNhzwkmD1wdXfNokyrLl268M0333Dx4kUqVKgAQFJSEps3b6Zr166sW7cujyctBUVDKURERKRQfPXVV9x99900bNiQ9u3bM2/ePNu0XWCdbmvKlCl06dKFRo0a0b17d+bOnUtSUoaV0uKuYkSe4ZP3FtGnV3cCGwbStk0bxo8bQ/SpP6zB7TV/Hz7GkHFTadzzIfqMeJaf9xyyjv/1KA9eVQAjvU7tg2l/72PMW7mWVM9K4Fke3Hz4KnQTAfXvYO++fYwYMYImTZowZ86cG55n/fr1qVGjBhs2bLCl/fTTTxiGQceOHTPlT0xMZNasWbRr146GDRvSt29fNm/enCnfZ599RufOnWncuDHDhw/n5MmTt3SdJXvqMRYREbnNbPrjPG9tOczxK7HULOvBU13r0jMwbwsf5NX777/PG2+8wfDhw5k8eTJHjx61BWyTnn4KUhMJv3ASH3cnpox/DC8PV06cOEnI+6u5fOkSs2bPthaUEMmrs+fy6fotDB/Um7bNGxIbl8CPu/cRlwKlrvUOJycnM+k/c3nowSE8UaUqy959j/EvvcHWrVsp7V365nWaNMmu/s888wz3338/o0ePts2beyPBwcGEhoYyYsQIwDqMolu3bri4uGTKO2nSJH7++WeeeuopatWqxdq1a3nyySdZtGgRXbp0AeCHH37gpZdeYsCAAfTu3ZuDBw8yYcKE3F3n685JMlNgLCIiUkwZhkF8cu56+jb/eZEJq/dhwroW2t8Xohnz4R7mD25CtwYVclyOm5ND/kyBZRjEREeyYMECHh0+lIlPPAypSbRt0wYnJydmz57NI/d2o7SLhYCyjjz/SH/brs0CquLmbGby60t5edo03NzcOH4xkk/WbebJMY/w+OjHMDu6goMTPYbYz12bnJzMpGefJSgoCICatWrTpUsXtm3bRt++fYmJibHW6dFHmThxIgBt27ZNr9Mjj1C6dPrDcYMHD2bUqFE5Pu0+ffoQEhLCqVOnKFOmDD/++COLFi0iISHBLt9ff/3Fd999x4wZMxg8eDAAHTp04OzZs3aB8ZIlS2jRogWzZs0CoH379iQmJrJ48WJbWbk9J8lMgbGIiEgxZBgGA5fu5LeT4be2/3XfJ6zel6v9W1QvzedjWt88ODYMsCRbx/g6e9gvMhF9Ec7vZ+8v+4iLi6PnXfVIiTht3eZWljZt2pCQkMDhE2dpWd8Pw+zEyi+/4bO1mzhz7jyJielDKE6fPo2/vz+79h3CMAzuuXcwhnMpyGYxB7PZTOvWrW2vq1atiqurKxcvXgRg79691jr17ElKSvp0a7Y6HT5My5YtbelZDYG4kRo1anDHHXcQGhpKlSpV8PDwoHXr1vzwww92+X777TcAevbsaZfeq1cvZs2aRVxcHC4uLhw8eJBnn33WLk+PHj3sAuPcnpNkpsBYRESkmCp2y30kxkBSTOZV3NLC7wqB9qusGSmAQXhkNAD9R03JstjzUclQMZCVK1bw+oJlPProo7Rq1QovLy8OHDjAf/7zHxITEwGIiIjA0dERX1/fG1bV1dUVZ2f7h+acnJxs5YSHW284+vfvn2lfgPPnz9u9Llu27A2Pl5U+ffrw5ZdfUrlyZXr16pXlimyRkZE4OTnh4+OT6XiGYRAdHU1sbCwpKSmZzvn6OuX2nCQzBcYiIiLFkMlk4vMxrXM9lKLfou0cvhiDkSHNZAL/CqX4+ok2mXcwDOtywxmXKk5Jxs2cjOnSn1DWPz3YTYiE2EtZH9jB2TorRMbA2L0slG+Ad41IABYuXEjFihUz7Vq1qnX1t02bNtG5c2eeeeYZ27ajR4/a5fXx8SElJYWrV69SuXLlm1+QbHh7e+eoTnnRu3dv5syZw7Fjx/joo4+yrUdycjKRkZG2OgFcuXIFk8lEqVKlcHFxwdHRMdO8w1euXMlUFhTsOd3uil1gfPToUWbOnMnevXvx8PCgb9++PPXUU5nu+jK6dOkSK1asYPv27Zw6dYpSpUpx5513MnHiRKpUqWLLt2PHDj7//HP2799PWFgYVapUYcCAAQwfPhwnp9t/HXERESlZTCYT7s65+6d6Yjd/xny4B5Mpfepdw4CnO1bDPSXS2tPrUQ4crpUbeTbrYDctHk9NSg92nT3A4pv1lGZZDblwdAFHF5o2bYabmxsXLlygW7du2dY9ISEh07/H69evt3t91113YTKZWLduHWPGjMnpZcmkadOmOapTXlSsWJHhw4dz9epVmjVrlmWe5s2bA9abgvvvv9+WvmnTJho0aIC7uzsADRo0YPPmzTz88MO2PN9++61dWYVxTre7YhUYR0ZGMnz4cGrUqEFISAgXL15k9uzZJCQk8PLLL2e738GDB9m8eTP33nsvjRs3Jjw8nCVLljBo0CBCQ0NtHz2sXr2ahIQExo8fT6VKldi/fz8hISEcPXrUNphdRESkRDEs1uDV7AxmMz0DK7F0kD/zfzzBsatJ1CrtyIRWpehZPgIiIqz7uHqlB8YOToDJfs5euzl8M8zX6+Zj/colLy8vxo8fzxtvvMGFCxdo2bIlDg4OnD59mu+//56QkBDc3Nxo06YNq1at4sMPP6RGjRqsW7cu05RkNWvW5P7772fx4sXExsbaxs/++OOPPPnkk7Z5g/OrTnk1ZUrWw0fS1KtXj+7du9vinZo1a7Ju3Tr27t1rN354zJgxPPHEE0yZMsU2K8XatWuL5JxuZ8UqMF69ejWxsbEsXLjQNtYmNTWVGTNmMHr06Gzf7M2bN2fjxo04OqafTrNmzejYsSNr1qxh5MiRAEyfPt1ufE6rVq2wWCy89dZbPPvsszcdryQiIlJkkhPSx/dmHONrSbZuL+tv7dEFegZ407NSxvGnJvug15RhrKtHWWsPcn7MQHEDI0eOpEKFCrz//vt8+OGHODo64ufnR8eOHW29xGPHjiU8PJwFCxYA1ofLpk6dmqlneOrUqVSoUIE1a9awatUqfHx8uPPOO/Hw8Mj3OhWGN954g7lz57Js2TIiIiKoVasWCxYsoHPnzrY8Xbp0YcaMGSxdupRvvvmGxo0b89ZbbzFo0KBieU4llckwDOPm2QrHgw8+iLe3t90dUlRUFC1btuS1115jwIABuSqvTZs23HPPPUyePDnbPD/99BOjRo1i3bp1BAQE5LrOBw4cAKBhw4a53vdWxMXFcejQIerXr2/7eEVKFrVhyac2LNmKXfslxZFw5STHryRQs2oFXB2N9KDXuxo4X6tjzCWIOptNIWbwrWFdvAIgJQGS4zMsV+xY4IFvYUpNTSUhIQFXV9csH2iT4i+/2zAhIYHjx49Ts2ZNXF0zr0qY03itWPUYHzt2jHvvvdcuzcvLi3LlynHs2LFclXX8+HHCwsKoXbv2DfPt2bMHZ2fnPA1INwyj0NY9j4+Pt/suJY/asORTG5Zshd5+SbGYIk9jijqDOfI0qbW7YnhXA8Bx70qcN08Gz2rQ9k2IMcAxPYC1JCdgOFgXhDA5uGJyLoXh6GwdNnEt6DUcnKyBL0Da6mYmJ3DO0DtosRTKqRaWtD49wzC0olsJld9tmJqaisViIT4+HksW73fDMHI0L3exCoyjoqLw8vLKlO7t7U1kZGSOyzEMg5kzZ1K+fHmCg4OzzXfixAlWrVrF4MGDc/3xS0bJyckcOnTolve/FSdOnCjU40n+UxuWfGrDki2/2s+cHIthdrQFsJ5h+yl/7Cuc4y/iEncBx+Qou/xHwxOIqNwRAO+IZOoAqQ7uWMxOpDi4kezsimF2wmJ2wmI4YNgWhHAAt+tWr0sFUlOAFP6N0qZek5Irv9owMTGRlJSUG3ak3mgihzTFKjDOLyEhIezatYt3330324/JYmJiePLJJ6latSpPP/10no7n5OREnTp18lRGTsXHx3PixAlq1KihAfQllNqw5FMblmy31H6xl3E496u15zfyDKao05gjz2CKOoMpIYLEvstIDegDgMM/x3HZ8bPd7oarD4ZXVSzeValSpyGVqte3bqhTg7jWg0gyucK5c5h8/DBf+xhYAwSyZxgGiYmJuLi45M/qfFLoCqIN08ZTZ7Xs9pEjR3JWRr7UJJ94eXkRHR2dKf36uf1u5LPPPmPRokW8+uqrdiveZJSUlMTYsWOJjIzk008/zfMYM5PJVOjj1Nzc3IrH2Di5ZWrDkk9tWLK5ubnh7uYG8eEQccr+K/I0tBwFtTtZM5/8Hb4emW1ZLolhkPZeqHkX9JoDPn7WMcI+1TC5emMCzFz/D691H3NCAmazGQcHB42ZzYG0j95NJpOuVwmV323o4OCA2WzGzc0tyzHGOQ2+i1VgXKtWrUxd4NHR0Vy+fJlatWrddP/Nmzczffp0xo8fz8CBA7PMY7FYmDRpEgcPHuSjjz6iUqVKWeYTEZHbgGFA7BWIvBbwVggEd+v89uYTP8GaR60zPWSlepv0wNi3FlRuZg12faqBT/VrP18Lfl080/fzrgqtRhfwiYlIQShWgXGHDh1YunSp3VjjTZs2YTabadu27Q333b17NxMnTmTQoEGMHTs223wzZszghx9+4L333rulWShERKQYSVu1zfHa2MErR2DXIog4nd77m5LhIbvuM6HJI9ZdXUunB8WeFewDXR8/a2CcpsIdMOqHQjopESkqxSowHjx4MB988AFjx45l9OjRXLx4kTlz5jB48GC7OYyHDx/OuXPn2Lx5M2BdLW/s2LHUqFGDvn37sm/fPlteX19f/Pz8AFi6dCmrV6/mkUcewdnZ2S5fnTp18PTMcMcvIiLFQ1IcXDhwbYhDxiEPp61DHjq/BG3GXcsbA78uv64AE5SqaA123dLnqzfKBsC436w9vE6ZP3oVkX+fYhUYe3t7s3LlSl555RXGjh2Lh4cHAwcOzPRwnMVisZvaY//+/URHRxMdHc2QIUPs8vbv35/Zs2cDsH37dgDee+893nvvPbt8q1atolWrVgVxWiIikh1LKkSfTw90I05BxEmo2w0a9LXmufI3LO+efRkRGVZG860JHZ617/n1rmpdmjhN2vSaji7gVTgPTotIyVCsAmOA2rVrs2LFihvm+eCDD+xeDxgwIEeLf1y/n4iIFLDUFIg+Zw143ctC+XrW9CtH4MMB1gUrLFlMNebskR4Yp43n9fZLH+7gUy39Z68q6fu5ekPnqQV/XiJyWyp2gbGIiJQghpG+olrcVdi91H58b9RZMK59wtdqDPR63fqzW+n0nl6zo7VXN2Pw63dX+jHcfeGpA4V3TiLyr6XAWEREsmdJhfAT6VOYZRzfG3HK2qvb8zVrXsOAn17PXIbZydrD65JhASd3XxixyZpeqhKYNeVWSZeTB9pnzZqVo094s/PVV18xZcoUtm/ffstziN9zzz38/ffffPTRR7Ro0eKW61JYdu/ezd69exkzZoxdekhICMuXL2fv3r15Kn/YsGH88ssv9O7dm3nz5tlti4mJoU2bNiQmJua57dL07duX+vXr8+qrr+Z4nzNnztClSxfmz59Pz54981yHG1FgLCLyb5YcD5Fn7OfwLRcAjQdbt8eHQ0iz7PcPP5H+s7sv3PkoeFW2H/bgWQHMZvv9TCaonvVc81Iyffrpp3av77//foYNG0afPn1saWkPwxeVw4cP8/fffwOwfv36EhEY//LLLyxfvjxTYDxo0CCCgoLy5Rju7u788MMPxMXF2c3NvmXLln/dPNEKjEVEbmdJcdaeXkxQzt+alhgDq/pag+DYS5n3qdcnPTB2L2MdG+xeJovxvdWhdI30/UwmCH6zoM9IiqkmTZpkSqtUqVKW6UVl/fr1mM1m7rzzTjZt2sTUqVNxcnIq6mrdkooVK1KxYsV8KatZs2YcPHiQrVu32t3IhIaG0rVrV9atW5cvxykJzDfPIiIixV5qCvyyDL57CT4bDu90gjm14bVKsKglfD8jPa+zB1z6Mz0odvKA8g2gbg+48zGof3d6XpMJnj0C436BoV9An7nQ7mkIvBeqtgCPsoV7nnJzEafh3L7MXxGni7RaP/74IyNGjKB169Y0a9aMQYMGsW3bNrs8UVFRTJ06lfbt29OwYUOCgoIyzUx1vS+//JLAwEA+//zzG+YzDIPQ0FDuuusuRowYQUREBD///HOmfIcPH+bBBx+kYcOGdO/enXXr1vHEE08wbNgwW56QkBCaNm3K33//zZAhQ2jcuDF9+vTJVJ7FYmHx4sV07tyZwMBAevbsyerVq+3yXLhwgQkTJtCmTRsaNmxI586dee2112zHWbhwIXFxcQQEBBAQEGCrR1odrr9+r7zyCh06dCAwMJDOnTvz5ps3v1l1dHSkR48ehIaG2tKuXr3Kzp077QLljFavXk2PHj1sx1m8eDEWi8Uuz549exgwYAANGzakT58+/PTTT1mWtXfvXh566CGaNGlC8+bNeeaZZwgLC7tpvQuCeoxFRIqzhCj7ZYozDnmoEAj9FlnzmR2sQXHGxSzSOJeyPuCWxmSC+z+41gtc3fog3I2WS83hUqpSDESchoXNISUx8zZHF+u8zT7VCr9eWMeJdurUiZEjR2I2m9m2bRujRo1i5cqVtulSZ82axc8//8wzzzxDlSpVuHz5cqbgOaMPP/yQN954g9dff53g4OAbHn/Pnj2cPXuWsWPH0q5dO3x8fAgNDaVz5862PAkJCYwcORIvLy/eeOMNABYtWkRUVFSmYSDJyclMmjSJhx56iCeeeIJly5Yxfvx4tm7dSunSpQGYM2cOq1at4vHHH6dp06b8+OOPTJs2jZSUFIYOHQrAc889x6VLl5g6dSplypTh/Pnz/PHHH4B1uMSFCxcIDQ1l5cqVANmuuZCUlMTw4cNt5+jv78+FCxf47bffbnhd0vTp04cRI0YQGRmJt7c3GzdupEKFCpmCb7DO8jVz5kyGDRtGx44d2bt3LwsXLiQ6Oprnn38egMuXL/PII48QEBDAW2+9RVRUFDNmzCAuLo769evbytq3bx/Dhw8nKCiIefPmER8fz1tvvcUTTzyRaXhOYVBgLCJSVAwDEiLsZ3FwKQXNhqVv/69/1sEugJGhd8ZkgqZDwcEpff7etGEPrj6Zg9s6XQvijKQgJMVmv83kYL84SdSZrINisKbHhaUHxjcs1wxOt/ZwW3bSAkGw9qS2atWKI0eO8Nlnn9kC4wMHDtCnTx/69+9vy5tdwLt8+XLeeecd5s+fT5cuXW56/NDQUFxcXOjevTtOTk706NGDdevWERsbi4eHB2DtfQ4LC+OTTz6hatWqAAQGBtK9e/dsA+O0cb41a9akS5cubNu2jb59+3L16lU+/PBDHnnkEZ588kkA2rVrR3h4OIsWLWLIkCE4ODhw4MABJk6cSO/evW1l9+vXD0gfLmE2m286JGXNmjX8+eefrF692i6YzXgtb6RFixaUKVOG7777jkGDBvHNN99k2VucmprKokWLCA4OZurUqbbzSk5OZvny5YwaNYrSpUuzcuVKTCYTy5Yto1SpUrbzefjhh+3Kmzt3LoGBgSxcuBDTtb9T/v7+th7m/BpHnVMKjEVECophWB9eS4i0LjyR5vOH4fI/1h7gxCj7fSo2Sg+MTSZrEBN7JUOgm+GrdE37fYP/W6CnI0XktcrZb6vbHR7MMIRgVb+cl/tWQ2ugnJXKTWHUjzkvKwcuXLjAvHnz2LFjB5cvX8YwDADuuOMOW54GDRrw9ddfU65cOdq3b4+/v3/WVX/rLUJDQ1m8eDHt27e/6bFTUlLYtGkTQUFBtiDt7rvv5tNPP2Xz5s22QPSPP/7A39/fFhQDVK1alXr16mUq02w207p1a7t8rq6uXLx4EYDff/+d5OTkTLMo9OrVi9DQUE6cOEHt2rVp0KABy5cvx8HBgbZt21K9evWbnk9Wdu7cSe3atbPs4c0Jk8lE7969+eabb2jXrh179uxh2rRpmfIdO3aM8PDwTOfVu3dv3n77bX7//XeCgoLYv38/rVq1sl1vgNatW+Pj42N7HR8fz969e3nuuefsFm6rUaMGlSpV4sCBAwqMRURKpL++gbCj9kMdIk9blyiu1BhGZ/g4+OJBuPJP+muPcum9vOUb2Jc75v/sV20TKYEsFguPP/440dHRjB8/nurVq+Pm5saCBQs4f/68Ld9LL72Et7c377//PnPmzKFSpUqMGjWKBx54wK687777jjp16tC8efMcHX/79u1cvXqVTp06ERVlvRn19/enXLlyhIaG2gLjS5cu4evrm2l/X19fEhPte+JdXV1xdna2S3NycrLli4yMBKBsWftx+GmvIyIiAJg3bx7z5s3jrbfeYsaMGdSsWZOJEyfSvfsNVnvMQkREBOXLl8/VPtcLDg5m5cqVrFixgrp16xIQEGC7XmnSzqtMmTJ26Wmv07Zfvnw5yyA/4/WNjo4mNTWVWbNmMWvWrEx5M743CosCYxGR7BgGxFy6FuSmz9/rcvU4NeNToX6Gh2g2T4Oww1mXc/1H291esY4JTluu2Nkj+zooKJYXzmW/zXTdVFoPrYHlOZzn9UaLppjy99n8kydP8ueff7Jo0SK6dk0fxpOQkGCXr1SpUrz44ou8+OKL/P3336xatYoZM2bg7+9vN7XaokWLGD9+PBMmTGDx4sU3nVli/fr1AEyZMoUpU6bYbQsPDycsLIwyZcpQvnx5Dh06lGn/q1ev2oZb5FRaz2hYWBgVKlSwpV+5csVue/ny5Zk1axYWi4U//viDJUuW8PTTT7Np0yaqVcv5eHAfHx/bVHS3KjAwkGrVqrFq1SomTJiQ7XHAek0ySntYztvbG4By5cpl+QBdxv1KlSqFyWRi9OjRdu+LNGljtQuTAmMR+feyWCDmYnoPb0pC+jAGgCVt4dLBTLs5AF5OXiRlTPTvATFNrhvfey3wvX6sZkDBTlAvt5kb3ThdzzEX44JzU24epfWiZgxgz549y969e6lRo0aW+wQEBDBlyhS++OILjh49ahcY16xZkyVLljB69GieeeYZ5s2bl+18u/Hx8Xz//fd07dqVhx56yG7blStXmDhxIhs2bGDYsGEEBgayZs0aTp8+bQtKz5w5w19//ZXj3uk0DRs2xMnJiU2bNtGgQfonQRs3bqRMmTKZzttsNtOoUSOeeuoptm7dysmTJ6lWrRpOTk4kJSVxM23atGHDhg3s37+fxo0b56quGT322GNs3bqVu+++O8vtNWvWxNfXl02bNtGtWze783JycqJRo0YANGrUiE8++YTo6GjbcIqdO3faesoB3NzcaNKkCceOHaNhw4a3XOf8pMBYRG5fllTrGErPDB8vbp0Jp3+xDnOIPAOpGf7BcS9jHxiXqgCXD0Gpynbz9ya6V+RkhIWq18ZIAtAj56s4iRQY9zLWTxmym5XCvUzm9EJQq1YtKlasyJtvvonFYiEuLo4FCxZk+uh/8ODBdOvWjbp16+Lg4MCaNWtwcnLKciGOunXr8u677zJixAief/555syZg/n6hWSA77//nri4OIYNG2Z7yC+jd999l9DQUIYNG8a9997L0qVLGTNmjO2BuYULF1K2bFnbg2E55evry9ChQ3nvvfdwdnamSZMm/PTTT4SGhvLSSy/h4OBAdHQ0jzzyCH379qVmzZokJyfzwQcf4OXlZQuma9euTUpKCitXrqRp06Z4enpSq1atTMfr27cvH3/8MaNGjWLcuHHUrVuXixcv8uuvv/LKK6/kuN4DBw5k4MCB2W53cHDgiSeeYObMmfj6+hIUFMS+fftYtmwZw4cPt/XyDh8+nI8//pjHHnuMxx57jKioKEJCQuzGGANMmjSJESNG8NRTTxEcHIyXlxcXLlxgx44dDBgwIMs2K0gKjEWk5Dv7m/VhNtv43pPpga+rNzx3LD3vmV/heIa5NE0O4FUlvYfXkpq+PPG971lniXCw/5g2NS6O6EOHNI2ZFD8+1axTsmX1UJ17mSKbqs3Z2ZmQkBD+85//MGHCBCpVqsTjjz/Orl27bFOTgXWhiTVr1nDmzBnMZjP+/v4sXbqU2rVrZ1lugwYNbMHxyy+/zCuvvJIpgA0NDaVy5crZBlj9+vXjtdde49SpU/j5+bF8+XKmTZvGpEmTqFChAk888QRr1qyxe4gsp5577jlKlSrFF198wdKlS6lSpQozZsxg8GDrAjouLi74+/vzwQcfcP78eVxdXQkMDOS9996zjcXt1KkTDzzwAO+88w5hYWHceeedfPDBB1le4xUrVjBv3jzefvttIiIiqFix4k2nsbsVw4YNw9HRkRUrVvDJJ59Qrlw5xo0bZ7c6X/ny5Vm2bBkzZ85kwoQJ+Pn58fLLL2dadrpp06Z8/PHHhISEMGXKFJKTk6lYsSJ33XXXLT+ImBcmw8jY5SG5deCAdYxWYX0EEBcXx6FDh6hfv77dso1ScqgNcyE12Rrc2ubvvfY9IQKGfJKeb1U/OPZD1mWYnWDyKXC+dq3/3midKSItEC5VGRxy10egNizZimP7JSQkcPz4cWrWrImrq+vNd/iXS01NJSEhAVdX1wJfsjgiIoKuXbvy8MMPM27cuAI91r9JfrfhzX6HchqvqcdYRIpOSqI18I04ZR3rm7YMMcAXj8DBr+zn6s0oKTZ9jGTVOwHj2pje66Y0K1UxvQcYIKBXgZ2OiJR877zzDmXLlrUtMLJ8+XJSU1O59957i7pqUggUGItIwUlOsF98YO+HcPSH9B7g6AtAhg+t6t+T3rPr7GENih1c7Mb32oLfjE/Nd36xUE5HRG5/ZrOZJUuWcPHiRRwcHGjcuDErV66kUqVKRV01KQQKjEUkbyJOXRvfezLz0sUxl6xTTaUFu6d2wR9f2O/v6JYe8CbFpOftOAU6vWid4zeLB2pERArCo48+yqOPPlrU1ZAiosBYRLKXGGMf6KYFv/2WpgewP82BvZkfBLGJPA3lAqw/N+gHZetmGOpQ3fpAUFYPsXmpd0ZERAqXAmORf7OEqPTAt3bn9MUkfnwddi+F+KtZ79dpKpS7tlRruXpQITDDMIfr5vF1yzBBe92u1i8REZFiSIGxyO0qbcKZtN7Yo1vhn++u9f6etM7wkBCRnn/cr9be3LR90oJiV59r43urpwe+bj7p+7UZZ/0SEREp4RQYi5RkCVFw9ViG+XszDns4BY9tTQ92z/wKu5dkLsPN1xrwJsenpzV5EAJ6WwNiV+/CORcREZEipsBYpLgyDOsk/Rkfaos4Be2eti4zDLBrCfz4WvZlRJxMD4yrt4U2T6b3/Kb1/rp4Zt7Pu4r1S0RE5F9EgbFIUTEMiL5o7eEtWze9Z/b3z2HbG9YgOCU+834BvdMDYx8/8KyQxdje6tbe3tI10ver0db6JSIiIllSYCxSGC4etK64Fnkal7Dj3HH5KC4br0BKgnX70K+gThfrz5ZkuPL3tR1N1gUqMvbwpgXFYF0Qo8mQQj0VERGR25UCY5FbZUm1LlBhN743w8993oJaQda85/fD1lcAcLj2BVgXqShV2boCXJpanWDYmmuBcNX0mSKyktU0ZyIiRSAgIOCmeWbNmsWAAQNu+RhfffUVU6ZMYfv27bi5ueVq33Xr1rFq1SqOHz+OYRhUqFCBZs2aMXHiRMqUKXPLdbpVZ86coUuXLsyfP5+ePXsCkJSUxLRp0/jhhx8IDw9nypQpREdHs3z5cvbu3Zun4w0bNoxffvmF3r17M2/ePLttMTExtGnThsTExDy3UZq+fftSv359Zs+eneN9sromhU2BsUh2UlMg+px94NugL5Svb92+fzWsfSL7/cOPA9cC4wqB0PgB8PEj0b0CJyIs+DVsh1uF2uDgZL+fVyXN4SsiJc6nn35q9/r+++9n2LBh9OnTx5bm5+dX2NUCYNmyZbz55ps8/PDDjB8/HsMwOHz4MOvXr+fSpUtFEhiXL1+eTz/9lBo1atjS1q5dy9q1a5k9ezZ+fn5UqVKF1NRUgoKC8uWY7u7u/PDDD8TFxeHu7m5L37JlCw4ODjfY899DgbH8e6UmQ9RZ69jetLl2T+6ErTOtQXDUWTBS7fcpVSk9MPbxA7OjtVfXu5r9Q20+1aB8g/T9KjWC/tYZIVLj4og5dAjDxy9zUCwiUkI1adIkU1qlSpWyTC9sH3zwAf3792fy5Mm2tKCgIB599FEsFkuR1MnZ2TnTtTl27Bjly5fnnnvusUuvWLFivhyzWbNmHDx4kK1bt9rdsISGhtK1a1fWrVuXL8cpybTOqtz+os7DbyutAe9Xo2B5L5h7B8wsD/Mbw58Z/hBYkuHk/1mHRBipYHYC31pQqyM0HQa+NdPzVm8DUy/BhP3wcCj0WwQdn7eO+a3RDtx9C/1URUTS7Dy3k75r+rLz3M6irgoAP/74IyNGjKB169Y0a9aMQYMGsW3bNrs8UVFRTJ06lfbt29OwYUOCgoJ4+umnb1jul19+SWBgIJ9//nm2eaKioihfvnyW28wZlpzv3Lkz//nPf3j33Xdp3749jRs35vHHH+fSpUt2+yQlJTF37lw6depEYGAgvXr1Yv369ZnK3rt3LyNHjqRZs2Y0bdqUQYMGsX37dsA6bCAgIIBNmzbZjr18+XLOnz9PQEAAAQEBnDlzhpCQEJo2bZrpfF555RU6dOhAYGAgnTt35s0337zhdQJwdHSkR48ehIaG2tKuXr3Kzp077QLljFavXk2PHj1sx1m8eHGmm4k9e/YwYMAAGjZsSJ8+ffjpp5+yLGvv3r089NBDNGnShJYtW/LCCy8QFhZ203oXJvUYS8mVHA+RZ9IXq8g4h2+r0RB4rzVf2GFYPz7rMhxcICkm/XWFQBjwbnrPr2cFMGdz/2jWx04iUvDikuNyvY+T2Yn5e+ZzLPIY8/fMp1HZRjiYHXB1dM1xue5O7jfcnltnzpyhU6dOjBw5ErPZzLZt2xg1ahQrV66kVatWgHUM8s8//8wzzzxDlSpVuHz5cqbgOaMPP/yQN954g9dff53g4OBs891xxx2sXr2aqlWr0rFjR8qVK5dt3s2bN1OlShWmT59OVFQU//3vf3nyySfthopMmDCBPXv2MHbsWGrXrs1PP/3Es88+i5eXl23Yw2+//cbw4cNp0qQJM2fOxMvLiz/++INz585ledyFCxeybNky/ve//7Fw4UKALIP5pKQkhg8fztmzZxk7diz+/v5cuHCB3377LdtzyqhPnz6MGDGCyMhIvL292bhxIxUqVMgUfIO1p33mzJkMGzaMjh07snfvXhYuXEh0dDTPP/88AJcvX+aRRx4hICCAt956i6ioKGbMmEFcXBz169e3lbV3716GDRtGUFAQ8+bNIzY2lrfeeotx48bx2Wef5ajuhUGBsRRfSXEZFqs4CZWbQpXm1m0ntsOK3tnvW7ND+s++taBOt/QhDmnTmXlXA49y9oGvuy80GlQw5yMicgtafdwq1/s81vAxDoYdBOBg2EHu+uQuWlRowfs937fl6fllT8ITw7Mt48DwA7mv7A0MHTrU9rPFYqFVq1YcOXKEzz77zBYYHzhwgD59+tC/f39b3uwC3uXLl/POO+8wf/58unTpcsNjT5s2jXHjxjF16lQAqlatSqdOnXj44YepWrWqXd7Y2FiWLVtGqVKlAOswhocffpiff/6Z9u3bs2vXLrZu3cp7771Hu3btAGjbti2XL18mJCTEFhi/8cYbVK9enZUrV9rG76blz0qDBg0oW7ZslkMsMlqzZg1//vknq1evtgtmM16zG2nRogVlypThu+++Y9CgQXzzzTdZ9hanpqayaNEigoODbdetXbt2JCcns3z5ckaNGkXp0qVZuXIlJpMpy2uW0ZtvvklgYCALFy7EZDKRmppK9erVGTRoED/99FO+jaPOKwXGUnQSY6zDFdLm7716DLbMSH/QLe6Kff4Oz6UHxl6Vrd+dPDKM683wValR+n7eVWHoFwV/PiIixcSmE5swm8xYDAsmTBgYGGnLxBeRCxcuMG/ePHbs2MHly5dt9bnjjjtseRo0aMDXX39NuXLlaN++Pf7+/lmW9dZbbxEaGsrixYtp3779TY/t7+9PaGgoO3fu5P/+7//43//+xwcffMBXX33FRx99ZNez2apVK1uAB9C6dWt8fHzYv38/7du3Z/v27fj4+HDXXXeRkpJiy9emTRumT59OamoqSUlJ7N+/n4kTJ+b7Q207d+6kdu3aWfbw5oTJZKJ379588803tGvXjj179jBt2rRM+Y4dO0Z4eHim2SF69+7N22+/ze+//05QUBD79+/P9pqliY+PZ8+ePTz33HOkplqf3UlNTcXPz4+KFSty4MABBcbyL5EYDcd/tl+mOO0r/ioEPQ+dXriW2QR/rrHf37kUlL7Wu1umdnq6jx88d9z60JymLBOR29juB3bnKv+uc7uY8OME22sDawA6/I7hdvk23bsp75XLIYvFwuOPP050dDTjx4+nevXquLm5sWDBAs6fP2/L99JLL+Ht7c3777/PnDlzqFSpEqNGjeKBBx6wK++7776jTp06NG/ePMd1cHZ2JigoyBaA/fzzz4wePZpFixbZhi4AWc5Q4evry+XLlwEIDw8nIiLCLqDP6PLly5hMJiwWS7bjmvMiIiIiz+UGBwezcuVKVqxYQd26dQkICCAqKsouT2RkJJD5eqS9Ttt++fJlqlevnukYvr7pz9lERUWRmprKrFmzmDVrVqa8Gd8DRU2Bsdwaw4CEiGtB7nXje+t0gRYjrfliL8PqGyxAEXMx/WfvqtBjlv3MDq4+WQe+Zgc93CYi/wq5GetrGAbvHHjH1kucxoSJt39/m47VOmK69jc1v8cQ38jJkyf5888/WbRoEV27drWlJyQk2OUrVaoUL774Ii+++CJ///03q1atYsaMGfj7+9OiRQtbvkWLFjF+/HgmTJjA4sWLcXLK/Qw/7du3p169ehw9etQuPauHwa5evWobl+zt7Y2vry/vvPNOluX6+vqSkpKC2WzO9NBefvDx8eHvv/++ecYbCAwMpFq1aqxatYoJEyZkmSetx/fq1at26WnXx9vb+mlvuXLlsr1maUqVKoXJZGL06NG29rdYLCQlJeHs7Fwk0+VlR4GxZM0wID782oNtp8CzIvhdG+cWeQYWt4bEqKz3dSmVHhh7VbWODbZbrjht+eJq6cMowDp1WesbzAssIiI3lGxJ5kLsBbugGKy9xhdiL5BsScbZwbnQ65WYaF3EKGMAe/bsWfbu3Ws3j29GAQEBTJkyhS+++IKjR4/aBcY1a9ZkyZIljB49mmeeeYZ58+bdcMjClStXKFu2rF1aQkIC58+fp06dOnbpu3fvJjo62jY0YOfOnURERNC4cWPAOmTi3XffxcnJiXr16mV5vLRxwmvXrmXkyJH5OpyiTZs2bNiwgf3799vqdCsee+wxtm7dyt13353l9po1a+Lr68umTZvo1q2bLX3jxo04OTnRqJF1yGKjRo345JNPsrxmadzd3WnSpAnHjh2jYcOGgHUoRUJCAq6ursVqDmUFxiVFxGmIC8OUkIBbxHFMF5LB1RXcy1gDzNwyDOtqa07XnlBOiITvX8mwgttp+9kaGj+QHhh7lLMOkQBwL5vF+N4m6fs5OsOoH2/ljEVEJJecHZxZ3Wc1VxOuZtrm6+pbJEExQK1atahYsSJvvvkmFouFuLg4FixYkGlIwODBg+nWrRt169bFwcGBNWvW4OTkZBcUp6lbty7vvvsuI0aM4Pnnn2fOnDl2U69ldPfdd9OpUyfatWtH+fLluXjxIh9++CHh4eEMH24/xMTDw4PHHnuMxx57jOjoaP773//SqFEj21jmtm3b0qlTJx599FEeffRRAgICiI+P58iRI5w8eZJXX30VgGeeeYaHH36Yhx9+mAceeABvb28OHjxI6dKlGThw4C1fy759+/Lxxx8zatQoxo0bR926dbl48SK//vorr7zySo7LGThw4A3r4eDgwBNPPMHMmTPx9fUlKCiIffv2sWzZMoYPH07p0tb5/4cPH87HH39su2ZRUVGEhITYjTEGeO655xg+fDhPPfUUwcHBeHp6cvr0aX799Vfuvfde2wOYRU2BcUkQcRoWNoeURNyABgA/X9vm6ALjfss6OE5NgXN77Zcpzjjs4Y7+0G/xtXJc4X/LMpfhWcHau1u6RnqaowuM+9W6OpuzR76eqoiI5E1Fj4pU9MifBSHyi7OzMyEhIfznP/9hwoQJVKpUiccff5xdu3bxxx9/2PI1a9aMNWvWcObMGcxmM/7+/ixdupTatWtnWW6DBg1swfHLL7/MK6+8YhsqktG4ceP44YcfmD17NlevXqV06dIEBASwYsUK7rrrLru83bp1o2LFikybNo2oqCjatGnDjBkz7PIsWLCAd955h08++YSzZ89SqlQp6tata7eUcosWLVi1ahVvvfUWU6ZMwWw2U7duXZ566qk8XEnrtVyxYgXz5s3j7bffJiIigooVK95wurpbNWzYMBwdHVmxYgWffPIJ5cqVY9y4cYwZM8aWp3z58ixbtoyZM2cyYcIE/Pz8ePnllzMtO92sWTM+/vhjQkJCmDJlCsnJyZQvX57WrVtnOUa5qJiMon5MtYQ7cMA6nU3aRwMF4tw+eOcGT2t2egnMJmvAW6YOtHnSmp6SaF3EIjs12lsXpkiz7b/XeqDThjtUBafcrUUvNxcXF8ehQ4eoX7++3ZKcUnKoDUu24th+CQkJHD9+nJo1a+Lq6nrzHf7lCupj+M6dO9OxY0defvnlfCtTspbfbXiz36GcxmvqMb4d/JDho5Ma7dMDY0cXqNzM2hvsU+268b3XAt+MOkwqvDqLiIiIFDMKjG8HFZtAhQbW4LfCddPHjPqhSKokIiIiUtIoML4d3DMfKjcp6lqIiIiUaFu3bi3qKkgRy/rxTRERERGRfxkFxiWBexnreOGsOLpYt4uIiIhInmgoRUngU806JVtcGPEZnrp0y8s8xiIiUuxooiiRW5NfvzsKjEsKH+tKcUZcHPHhThgV60MxmWZIRETyJm1FuLi4ONzcNE2mSG7FxcUB3NLy4BkpMBYRESliDg4O+Pj4cOnSJcC6hG5WC1WIVWpqqm2Z6eK0nLDkXH61oWEYxMXFcenSJXx8fPL8flBgLCIiUgxUrGhdrS4tOJbsWSwWUlJScHR0zHYZaCne8rsNfXx8bL9DeaHAWEREpBgwmUxUqlSJ8uXLk5ycXNTVKdbi4+M5duwYfn5+GnpSQuVnGzo5OeXbJwcKjEVERIoRBwcHDQ+4CYvFAoCLi4uW0C6himsb6vMHEREREREUGIuIiIiIAAqMRUREREQAMBmaTTxP9uzZg2EYODs7F8rxDMMgOTkZJycnTeVTQqkNSz61Ycmm9iv51IYlX2G3YVJSEiaTiWbNmt0wnx6+y6PC/oU0mUyFFoRLwVAblnxqw5JN7VfyqQ1LvsJuQ5PJlKOYTT3GIiIiIiJojLGIiIiICKDAWEREREQEUGAsIiIiIgIoMBYRERERARQYi4iIiIgACoxFRERERAAFxiIiIiIigAJjERERERFAgbGIiIiICKDAWEREREQEUGAsIiIiIgIoMBYRERERARQYFysnT57k5Zdfpm/fvjRo0IA+ffrkaD/DMHjnnXfo2LEjjRo14v7772ffvn0FW1nJ5Fba79KlS8yZM4e+ffvStGlTOnTowDPPPMPZs2cLocZyvVv9HcxoxYoVBAQEMHr06AKoodxMXtrw4sWLPP/889x11100atSIXr16sW7dugKsrWTlVtswPDycl19+mY4dO9KkSRP69OnDJ598UsC1lett3LiRxx9/nA4dOtCkSRP69u3LF198gWEYN9yvuMQyjoV+RMnW4cOH+emnn2jcuDEWi+Wmb6I0y5YtY8GCBUyaNImAgAA++ugjRo4cydq1a6lWrVoB11rS3Er7HTx4kM2bN3PvvffSuHFjwsPDWbJkCYMGDSI0NBRfX99CqLmkudXfwTSXL19m0aJFlClTpoBqKDdzq2146dIl7r//fmrWrMkrr7yCp6cnhw8fJikpqYBrLNe71TacMGECx44dY+LEiVSqVIlt27Yxffp0HBwcuO+++wq41pJmxYoVVKlShcmTJ1O6dGl27NjBSy+9xIULFxg3bly2+xWXWMZk5PYvvxQYi8WC2WztxJ88eTJ//PEHoaGhN9wnMTGRNm3a8OCDDzJx4kQAkpKS6NmzJx06dGD69OkFXW255lbaLyoqCnd3dxwd0+9RL1y4QMeOHXnuuecYOXJkgdZZ7N1KG2b03HPPYTKZOHfuHO7u7rz99tsFVVXJxq224bPPPsvp06f56KOPcHBwKOhqyg3cShtevnyZdu3aMWvWLAYMGGBLHzp0KA4ODqxcubJA6yzprl69mqlT56WXXmLDhg3873//s7VtRsUpltFQimIkqzfLzezZs4eYmBh69eplS3N2dqZbt25s27YtP6snN3Er7efl5WUXFANUrFgRX19fLl26lF9Vkxy6lTZM8+uvv7JlyxaeeeaZfKyR5NattGFMTAwbN27kgQceUFBcDNxKG6akpABQqlQpu3RPT89cf/IjeZPVJ53169cnJiaGuLi4LPcpTrGMAuMS7tixYwDUqlXLLr127dqcO3eOhISEoqiW5MHx48cJCwujdu3aRV0VyaHU1FReeeUVxowZQ/ny5Yu6OpJLBw8eJDk5GUdHR4YOHcodd9xB27ZteeONN0hOTi7q6kkOVKpUiXbt2rF06VKOHDlCTEwMGzZsYPv27Tz44INFXb1/vd9++40KFSrg6emZ5fbiFMtojHEJFxUVhbOzMy4uLnbpXl5eGIZBZGQkrq6uRVQ7yS3DMJg5cybly5cnODi4qKsjOfTxxx8THx/Pww8/XNRVkVtw5coVAKZOncp9993HuHHj+P3331mwYAFms1mfApQQISEhPP3007a/nQ4ODkydOpUePXoUcc3+3X799Vc2bNjA888/n22e4hTLKDAWKUZCQkLYtWsX7777Lu7u7kVdHcmBsLAwFixYwOuvv46zs3NRV0dugcViAaBNmzZMnjwZgLvuuovY2FiWL1/O2LFj1cFQzBmGwZQpUzhx4gRvvvkm5cqVY8eOHbz22mt4e3uro6GIXLhwgaeffppWrVrx0EMPFXV1ckSBcQnn5eVFUlISiYmJdndaUVFRmEwmvL29i7B2khufffYZixYt4tVXX/3/9u42puryj+P4G21EDgmpwQZEmslNK2QOJF07pfI83ZCG09mG5AzEYsupW7ertNYsYWttKcObcvqAXD4Q72Y0oLW2NvNB8w60iRsmrnFOiqTSA+b5/0/4T+MfHI6+XxsPfte5Dnx/5zd2PrvO9zo/Zs2aFe1ydIc2bdpETk4OhYWF9Pb2AoP9jteuXbvl5kqNPUlJScBgGP5vs2bN4rPPPuPs2bPk5OREozTdoW+++Ybm5ma+/vrr8LUqLi6mp6eHDRs2GIyjoLe3l8rKSpKTk6mvr//b3vGxlGXsMY5xN/txOjs7I8Y7OjpIT093lSNGHDx4kLfeeouamhpKS0ujXY7+gc7OTn744QeKiorCPz/++COtra0UFRXR3t4e7RJ1G48//vjfPn716tVRqkTDderUKcaPH092dnbEeF5eHhcuXODKlStRquze1NfXx/LlywkGg2zevHnIpsi/GktZxmWMGDdjxgwSExPZt28fubm5APzxxx8cOHCAQCAQ5ep0J77//ntqa2tZuHAhVVVV0S5H/9C6devCK8U3vf/++yQkJFBbW+tKYwzIyMggOzub9vZ2Fi9eHB5vb28nISHhtsFZ0ZeRkcH169c5fvx4+L0QBjdWPvTQQzzwwANRrO7ecu3aNV555RU6Ojr44osvSEtLu+1zxlKWMRiPIVeuXKGlpQWArq4uQqEQzc3NAMycOZOUlBSWLl3K+fPnOXjwIAD3338/y5cvp76+npSUFLKzs9m5cye//fYbFRUVUTuXe9Fwrt/p06epqqpi8uTJPP/88xF3+UlJSSErK2vUz+NeNpxrmJeXN+T3JCUlMWHCBIqLi0eveAHDu4YAr776Ki+//DLvvfcezz33HMeOHaOhoYGKigr7/UfZcK5hIBAgPT2dmpoaqqqqSE1NpbW1la+++oqVK1dG7VzuRW+//TZHjhxhzZo1hEKhiPe1J554gvj4+DGdZQzGY0hPTw+rVq2KGLt5vG3bNoqLi7lx4wbXr1+PmFNZWcnAwAANDQ1cunSJvLw8tmzZ4l3vRtlwrt/Ro0cJBoMEg0HKy8sjnrtgwQI2bNgw8oUrbLj/gxo7hnsN586dy8aNG/n000/ZuXMnqamprFy5kpdeemnUateg4VzDxMREGhsb+fjjj/noo48IBoNkZmayZs2aiE8BNPLa2toAbvn+dfjwYTIzM8d0lvHOd5IkSRJuvpMkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpKkf1lTUxM5OTkcO3Ys2qVI0j/ine8kKQY1NTWxdu3a//n4rl27KCgoGL2CJOkuYDCWpBhWU1NDZmbmkPGsrKwoVCNJsc1gLEkxLBAI8NRTT0W7DEm6K9hjLEl3qXPnzpGTk8OWLVtobGxkzpw55Ofns3jxYk6cODFk/nfffceiRYsoKCigsLCQFStWcPr06SHzuru7WbduHc888wxPPvkkc+fO5c0336S/vz9iXn9/P+vXr+fpp5+moKCAqqoqLl26NGLnK0n/L1eMJSmGhUKhIWEzLi6OSZMmhY/37NnD77//zqJFi7h69Srbt29n6dKl7N27l4cffhiA9vZ2KisryczMpLq6mr6+Pnbs2EF5eTlNTU3hdo3u7m5KS0sJBoOUlZXx2GOP0d3dzf79++nr6yM+Pj78d999912SkpKorq6mq6uLrVu38s477/DJJ5+M/AsjScNgMJakGPbiiy8OGYuPj4/4RohffvmFAwcOkJaWBgy2XyxcuJDPP/88vIHvww8/5MEHH2TXrl0kJycDUFJSwoIFC6ivr+eDDz4AYOPGjVy8eJHdu3dHtHCsWrWKgYGBiDqSk5NpaGggLi4OgBs3brB9+3aCwSATJ078114DSfq3GIwlKYa98cYbTJkyJWJs3LjILrmSkpJwKAbIz89n+vTptLS0sHbtWi5cuMDPP//MsmXLwqEYIDc3l9mzZ9PS0gIMBttDhw4xZ86cW/Y13wzAN5WVlUWMFRYW0tjYSFdXF7m5ucM+Z0kaKQZjSYph+fn5t9189+ijjw4Zmzx5Mvv27QPg/PnzAEMCNsDUqVNpbW3l8uXLXL58mVAoxLRp0+6otvT09IjjpKQkAHp7e+/o+ZI02tx8J0kaEX9dub7pry0XkjRWuGIsSXe5s2fPDhk7c+YMGRkZwH9Wdjs7O4fM6+joYNKkSUyYMIGEhAQSExM5efLkyBYsSVHiirEk3eUOHTpEd3d3+Pinn37i6NGjBAIBAFJTU8nLy2PPnj0RbQ4nTpygra2NZ599FhhcAS4pKeHIkSO3vN2zK8GSYp0rxpIUw7799ls6OjqGjM+YMSO88S0rK4vy8nLKy8vp7+9n27ZtJCcns2zZsvD81atXU1lZyQsvvEBpaWn469omTpxIdXV1eF5tbS1tbW0sWbKEsrIypk6dyq+//kpzczNffvlluI9YkmKRwViSYlhdXd0tx9evX8/MmTMBmD9/PuPGjWPr1q309PSQn5/P66+/Tmpqanj+7Nmz2bx5M3V1ddTV1XHfffdRVFTEa6+9xiOPPBKel5aWxu7du9m0aRN79+4lFAqRlpZGIBAgISFhZE9WkkZY3ICffUnSXencuXPMmzeP1atXU1FREe1yJGnMs8dYkiRJwmAsSZIkAQZjSZIkCbDHWJIkSQJcMZYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIA+BNZ443TwIr5cgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_metric('eval_accuracy')"
      ],
      "id": "028bc464"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "It30-zwltsir"
      },
      "outputs": [],
      "source": [
        "# ts_val_metrics = ts_trainer.evaluate(eval_dataset=encoded_dataset[\"valid\"])"
      ],
      "id": "It30-zwltsir"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9a48ff20"
      },
      "source": [
        "# Using our models"
      ],
      "id": "9a48ff20"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dPPAXnbjtr5N"
      },
      "outputs": [],
      "source": [],
      "id": "dPPAXnbjtr5N"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "849d1ccd"
      },
      "outputs": [],
      "source": [
        "# Our base model\n",
        "teacher_model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    f\"teacher-bert\", problem_type=\"multi_label_classification\",\n",
        "    id2label=id2label,\n",
        "    label2id=label2id,\n",
        ").eval().to(0)\n",
        "\n",
        "# Our task specific model\n",
        "task_specific_model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    'task-specific-distilbert',\n",
        "    num_labels=len(unique_labels),\n",
        "    id2label=id2label,\n",
        ").eval().to(0)"
      ],
      "id": "849d1ccd"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IgVCjwDJELTd"
      },
      "outputs": [],
      "source": [],
      "id": "IgVCjwDJELTd"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "51ba896d",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "batch = data_collator(list(encoded_dataset['test'].select_columns(['labels', 'input_ids', 'attention_mask', 'token_type_ids']))[:32]).to(0)"
      ],
      "id": "51ba896d"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LToY7TIowBkZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b456153f-c1c0-480f-d06e-152b1c9bc936"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "66.6 ms ± 6.37 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
          ]
        }
      ],
      "source": [
        "%timeit teacher_model(**batch)"
      ],
      "id": "LToY7TIowBkZ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "68746d8b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf19dc26-d3a5-4de7-d213-3c5cc9c4d488"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:00<00:00, 15.38it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.062432360649108884"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "import time\n",
        "from tqdm import tqdm\n",
        "\n",
        "time_to_run_teacher = []\n",
        "TIMES_TO_RUN = 10\n",
        "\n",
        "for _ in tqdm(range(TIMES_TO_RUN)):\n",
        "    start_time = time.time()\n",
        "    teacher_model(**batch)\n",
        "    end_time = time.time()\n",
        "    time_to_run_teacher.append(end_time - start_time)\n",
        "\n",
        "sum(time_to_run_teacher) / len(time_to_run_teacher)"
      ],
      "id": "68746d8b"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2e7469b3"
      },
      "outputs": [],
      "source": [
        "distil_batch = {k: v for k, v in batch.items() if k != 'token_type_ids'}"
      ],
      "id": "2e7469b3"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4830534f",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "917bc1ab-f1fa-4d8a-cafc-6b0a6998f8ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14.8 ms ± 2.22 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
          ]
        }
      ],
      "source": [
        "%timeit task_specific_model(**distil_batch)"
      ],
      "id": "4830534f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jo-s0zCJwGsA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e05a0a45-e633-43f6-aec9-153ccea4c1c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:00<00:00, 81.31it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.011610031127929688"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "import time\n",
        "from tqdm import tqdm\n",
        "\n",
        "time_to_run_student = []\n",
        "TIMES_TO_RUN = 10\n",
        "\n",
        "for _ in tqdm(range(TIMES_TO_RUN)):\n",
        "    start_time = time.time()\n",
        "    task_specific_model(**distil_batch)\n",
        "    end_time = time.time()\n",
        "    time_to_run_student.append(end_time - start_time)\n",
        "\n",
        "sum(time_to_run_student) / len(time_to_run_student)"
      ],
      "id": "Jo-s0zCJwGsA"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eb64cedd"
      },
      "outputs": [],
      "source": [
        "# ^^ shows how our distilled model is over 6x faster"
      ],
      "id": "eb64cedd"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0bd06113",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b19604a-3d88-46ca-bf0e-8e634dd7a33c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trained Model has 333606939 parameters.\n",
            "Data type of the parameter in teacher_model: torch.float32\n",
            "Task Specific Model has 65802267 parameters.\n",
            "Data type of the parameter in task_specific_model: torch.float32\n",
            "Estimated memory for teacher_model: 1272.6094779968262 MB\n",
            "Estimated memory for task_specific_model: 251.01572799682617 MB\n"
          ]
        }
      ],
      "source": [
        "# Count the number of parameters in the teacher_model\n",
        "num_params_trained = sum(p.numel() for p in teacher_model.parameters())\n",
        "print(f'Trained Model has {num_params_trained} parameters.')\n",
        "\n",
        "# Print the data types of the parameters in the teacher_model\n",
        "for p in teacher_model.parameters():\n",
        "    print(f'Data type of the parameter in teacher_model: {p.dtype}')\n",
        "    break  # We break after printing the first one assuming all parameters have the same dtype\n",
        "\n",
        "# Count the number of parameters in the task_specific_model\n",
        "num_params_task_specific = sum(p.numel() for p in task_specific_model.parameters())\n",
        "print(f'Task Specific Model has {num_params_task_specific} parameters.')\n",
        "\n",
        "# Print the data types of the parameters in the task_specific_model\n",
        "for p in task_specific_model.parameters():\n",
        "    print(f'Data type of the parameter in task_specific_model: {p.dtype}')\n",
        "    break  # We break after printing the first one assuming all parameters have the same dtype\n",
        "\n",
        "# Estimate the memory usage (in MB assuming parameters are stored as 32-bit floats)\n",
        "memory_usage_trained = num_params_trained * 4 / (1024 ** 2)\n",
        "memory_usage_task_specific = num_params_task_specific * 4 / (1024 ** 2)\n",
        "\n",
        "print(f'Estimated memory for teacher_model: {memory_usage_trained} MB')\n",
        "print(f'Estimated memory for task_specific_model: {memory_usage_task_specific} MB')\n"
      ],
      "id": "0bd06113"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e3e3de3e"
      },
      "outputs": [],
      "source": [],
      "id": "e3e3de3e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b641a5ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "cc1c6671-59e9-4475-a4e9-53ec39881026"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Model names\n",
        "models = ['Teacher Model', 'Task-specific Model']\n",
        "\n",
        "# Estimated memory usage (in MB)\n",
        "\n",
        "\n",
        "# Create a figure and a set of subplots\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Generate a bar plot with patterns\n",
        "bars = ax.bar(models, [0.219975, 0.247680], align='center', alpha=0.5, color=['#1f77b4', '#ff7f0e'])\n",
        "\n",
        "# Add labels and title\n",
        "ax.set_ylabel('Jaccard Score')\n",
        "ax.set_title('Best Jaccard Score of Models')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n"
      ],
      "id": "b641a5ff"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aa7eb287"
      },
      "outputs": [],
      "source": [],
      "id": "aa7eb287"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "21e6d6b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "8dfd01ec-2ac8-4dc3-9ca9-d922fac42608"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Model names\n",
        "models = ['Teacher Model', 'Task-specific Model']\n",
        "\n",
        "# Estimated memory usage (in MB)\n",
        "# memory_usage = [memory_usage_trained, memory_usage_task_specific]\n",
        "memory_usage = [1272.6094, 251.015]\n",
        "# Create a figure and a set of subplots\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Generate a bar plot with patterns\n",
        "bars = ax.bar(models, memory_usage, align='center', alpha=0.5, color=['#1f77b4', '#ff7f0e'])\n",
        "\n",
        "# Add labels and title\n",
        "ax.set_ylabel('Memory Usage (MB)')\n",
        "ax.set_title('Memory Usage of Models')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n"
      ],
      "id": "21e6d6b6"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "53b9c230",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "501f9d3b-4652-4db2-addf-d5dc85fbdc77"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Model names\n",
        "models = ['Teacher Model', 'Task-specific Model']\n",
        "\n",
        "# Times taken to run a batch of 32 items (in seconds)\n",
        "times = [66.6, 14.8]\n",
        "\n",
        "# Standard deviations\n",
        "std_devs = [6.37, 2.22]\n",
        "\n",
        "# Create a figure and a set of subplots\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Generate a bar plot\n",
        "ax.bar(models, times, yerr=std_devs, align='center', alpha=0.5, ecolor='black', capsize=10,  color=['#1f77b4', '#ff7f0e'])\n",
        "\n",
        "# Add labels and title\n",
        "ax.set_ylabel('Time (seconds)')\n",
        "ax.set_title('Time to run a batch of 32 items')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n"
      ],
      "id": "53b9c230"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6c24e62f"
      },
      "outputs": [],
      "source": [],
      "id": "6c24e62f"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yzqv53DtKKsQ"
      },
      "id": "Yzqv53DtKKsQ",
      "execution_count": null,
      "outputs": []
    }
  ],
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    "colab": {
      "gpuType": "V100",
      "provenance": []
    },
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